Image processing device, cell recognition device, cell recognition method, and cell recognition program

ABSTRACT

Provided is an image processing device including: a memory; and a processor comprising hardware, the processor configured to: calculate a feature value; detect, as peak positions, pixel positions the feature value of which are greater than a prescribed feature value threshold value; record the detected peak positions; form, one at a time for the detected peak positions, a cell region; identify a center position of the formed cell region; determine, by using at least one of the peak position, a morphology of the cell region, and the center position of the cell region, a proximity state between the currently formed cell region and a previously formed cell region; and correct, when it is determined that the proximity state is satisfied, at least one of the currently formed cell region and the previously formed cell region.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Japanese Patent Application No.2017-183154, the content of which is incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to an image processing device, a cellrecognition device, a cell recognition method, and a cell recognitionprogram for extracting individual cells from a cell image obtained byacquiring an image with, for example, a fluorescence microscope.

BACKGROUND ART

In the fields of medicine and life science, various cell analyses havebeen carried out using cell images acquired with a microscope. Inresearch on stem cells, such as Embryonic Stem Cells (ES cells) andInduced Pluripotent Stem Cells (iPS cells), in order to elucidate thecell differentiation mechanism, develop new drugs, and so forth, thework of observing the cell differentiation process and changes in cellmorphological features, examining the differences in properties amongcells, and so forth is generally carried out using a plurality of cellimages that have been acquired time-sequentially. In addition, regardingcell image analysis, it is also becoming possible to automate cumbersomework that has so far been carried out via visual observation, such asscreening of individual cells, by applying image analysis techniquesincluding image recognition.

Furthermore, for the purpose of, for example, studying the effects of,for example, drugs in an environment that more closely approximates theenvironment in a living body, it is becoming possible to grow athree-dimensional culture in which cells are cultured in the form of athree-dimensional object, and to three-dimensionally analyze the culturestate of a cell cluster composed of a plurality of three-dimensionallyaggregated cells by applying image analysis techniques (refer to, forexample, Patent Literature 1). With the application of such imageprocessing techniques, information on cell morphologies, the number ofindividual cells, and so forth can be recognized efficiently byautomatically detecting individual cells included in a cell image.

Patent Literature 1 discloses a method for calculating a score,representing a prescribed feature value, for each pixel in a cell imageon the basis of the pixel values in a prescribed region including thatpixel, placing an exclusive region, representing a region identical orproximate to the prescribed region, for each pixel at the position ofthat pixel in descending order of the pixel score, and detecting one ofthe thus-placed exclusive regions as an object of interest.

CITATION LIST Patent Literature

{PTL 1}

PCT International Publication No. WO 2005/057496

SUMMARY OF INVENTION

One aspect of the present invention is an image processing deviceincluding: a memory; and a processor comprising hardware, the processorconfigured to: calculate a feature value, the feature value representinghow likely a pixel value in each of pixels in a cell image formed bycapturing an image of a cell cluster composed of a plurality of cells isto be an extreme value; detect, as peak positions, pixel positions thefeature value of which are greater than a prescribed feature valuethreshold value in the cell image; record the detected peak positions inthe memory; form, one at a time for the recorded peak positions recordedin the memory, a cell region on the basis of a distribution of the pixelvalues of a plurality of pixels included in a region peripheral to thepeak position in the cell image; identify a center position of theformed cell region; determine, by using at least one of the peakposition, a morphology of the cell region, and the center position ofthe cell region, a proximity state between the currently formed cellregion and a previously formed cell region; and correct, when it isdetermined that the proximity state is satisfied, at least one of thecurrently formed cell region and the previously formed cell region.

Another aspect of the present invention is a cell recognition deviceincluding: an image acquisition device that is configured to acquire acell image formed by capturing an image of a cell cluster composed of aplurality of cells; and an image processing device that includes amemory and a processor, the processor configured to: calculate a featurevalue, the feature value representing how likely a pixel value in eachof pixels in the cell image acquired by the image acquisition device isto be an extreme value; detect, as peak positions, pixel positions thefeature value of which are greater than a prescribed feature valuethreshold value in the cell image; record the detected peak positions inthe memory; form, one at a time for the recorded peak positions recordedin the memory, a cell region on the basis of a distribution of the pixelvalues of a plurality of pixels included in a region peripheral to thepeak position in the cell image; identify a center position of theformed cell region; determine, by using at least one of the peakposition, a morphology of the cell region, and the center position ofthe cell region, a proximity state between the currently formed cellregion and a previously formed cell region; and correct, when it isdetermined that the proximity state is satisfied, at least one of thecurrently formed cell region and the previously formed cell region.

Another aspect of the present invention is a cell recognition methodincluding: calculating a feature value, the feature value representinghow likely a pixel value in each of pixels in a cell image formed bycapturing an image of a cell cluster composed of a plurality of cells isto be an extreme value; detecting, as peak positions, pixel positionsthe feature value of which are greater than a prescribed feature valuethreshold value in the cell image and recording the detected peakpositions; forming, one at a time for the recorded peak positions, acell region on the basis of a distribution of the pixel values of aplurality of pixels included in a region peripheral to the peak positionin the cell image; identifying a center position of the formed cellregion; determining, by using at least one of the peak position, amorphology of the cell region, and the center position of the cellregion, a proximity state between the currently formed cell region and apreviously formed cell region; and correcting, when it is determinedthat the proximity state is satisfied, at least one of the currentlyformed cell region and the previously formed cell region.

Another aspect of the present invention is a non-transitorycomputer-readable medium having a cell recognition program storedthereon, the cell recognition program causing a computer to executefunctions of: calculating a feature value, the feature valuerepresenting how likely a pixel value in each of pixels in a cell imageformed by capturing an image of a cell cluster composed of a pluralityof cells is to be an extreme value; detecting, as peak positions, pixelpositions the feature value of which are greater than a prescribedfeature value threshold value in the cell image and recording the peakpositions; forming, one at a time for the recorded peak positions, acell region on the basis of a distribution of the pixel values of aplurality of pixels included in a region peripheral to the peak positionin the cell image; identifying a center position of the formed cellregion; determining, by using at least one of the peak position, amorphology of the cell region, and the center position of the cellregion, a proximity state between the currently formed cell region and apreviously formed cell region; and correcting, when it is determinedthat the proximity state is satisfied, at least one of the currentlyformed cell region and the previously formed cell region.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing the configuration of a cellrecognition device according to a first embodiment of the presentinvention.

FIG. 2 is a diagram depicting one example of a spheroid in which cellsaggregate in the form of a three-dimensional agglomerate.

FIG. 3A is a diagram depicting one example of Z-stack image datacomposed of a set of spheroid images that have been acquired atprescribed intervals.

FIG. 3B is a diagram depicting a cross-sectional (slice) image at Z=z(2)in the Z-stack image data in FIG. 3A.

FIG. 4 is a diagram depicting an example of fitting a luminancehistogram to a mixture normal distribution.

FIG. 5 is a block diagram showing a specific configuration of a seedgeneration unit in FIG. 1.

FIG. 6 is a diagram showing one example of a spheroid in whichindividual cells appear to be expanded as a result of blurring takingplace in the Z direction.

FIG. 7 is a block diagram showing a specific configuration of a regiongeneration unit in FIG. 1.

FIG. 8A is a diagram showing one example of a situation in which acurrently formed cell region, which is very close to a previously formedcell region, is deleted.

FIG. 8B is a diagram depicting one example of a situation in which apreviously formed cell region and a currently formed cell region, whichlargely overlaps the previously formed cell region, are integrated.

FIG. 8C is a diagram showing one example of a situation in which apreviously formed cell region and a currently formed cell region, whichslightly overlaps the previously formed cell region, are separated fromeach other and are re-formed or formed.

FIG. 9 is a flowchart for illustrating a cell recognition methodaccording to the present invention.

FIG. 10 is a flowchart for illustrating details of region formationprocessing in FIG. 9.

FIG. 11 is a diagram for illustrating a plurality of peak pixelpositions at each of which the LoG output value locally exhibits amaximum value.

FIG. 12 is a diagram for illustrating a situation in which seedinformation is selected in descending order of the LoG output value.

FIG. 13 is a diagram for illustrating a situation in which cell regionsare formed, one for each set of seed coordinates.

FIG. 14 is a diagram depicting one example of a cell region divisionimage in which individual cell regions are delimited.

FIG. 15 is schematic diagram depicting one example of cell nuclei andcytoplasms constituting cells.

FIG. 16 is a block diagram showing the configuration of a cellrecognition device according to a second embodiment of the presentinvention.

FIG. 17A is a diagram depicting one example of a spheroid (raw image) inwhich cells aggregate in the form of a three-dimensional agglomerate.

FIG. 17B is a schematic diagram depicting cell nucleus regions.

FIG. 17C is a schematic diagram depicting cytoplasm regions.

FIG. 17D is a diagram depicting a superimposed image of cell nucleusregions and cytoplasm regions.

FIG. 17E is a diagram depicting a resultant region growth image.

FIG. 18 is a block diagram showing a specific configuration of a regiongeneration unit in FIG. 16.

FIG. 19 is a flowchart for illustrating a cell recognition methodaccording to the second embodiment of the present invention.

FIG. 20 is a flowchart for illustrating details of region formationprocessing in FIG. 19.

DESCRIPTION OF EMBODIMENTS First Embodiment

An image processing device, a cell recognition device, and a cellrecognition method according to a first embodiment of the presentinvention will now be described with reference to the drawings.

As shown in FIG. 1, a cell recognition device 1 according to thisembodiment includes: an image acquisition device (image acquisitionunit) 3 that acquires a cell image by capturing an image of a cellcluster composed of a plurality of cells; and an image processing device5 for processing the cell image acquired by the image acquisition device3.

The image acquisition device 3 includes: an image capturing element,such as a CCD, for acquiring an image of cells captured with afluorescence microscope; and an A/D converter for converting, into adigital signal, the cell image acquired by the image capturing element(neither is shown in the figure), and the image acquisition device 3outputs, for example, a 16-bit (0 to 65535 gradations) raw image signal.

In addition, the image acquisition device 3 is configured to be capableof acquiring a plurality of images at preset intervals in the verticaldirection and outputting, as a raw image signal, three-dimensionalZ-stack image data composed of a set of the images. In the followingdescription, the vertical direction is assumed to be the Z direction,and the horizontal directions that are orthogonal to the Z direction andthat are orthogonal to each other are assumed to be the X direction andthe Y direction, respectively.

FIG. 2 shows a spheroid S serving as one example of the cell cluster inwhich cells C aggregate three-dimensionally in the form of anagglomerate. In addition, FIG. 3A shows one example of the Z-stack imagedata composed of a set of images of the spheroid S acquired atprescribed intervals, and FIG. 3B shows one example of a cross-sectional(slice) image, corresponding to the depth Z=z(2), included in theZ-stack image data. The raw image signal output from the imageacquisition device 3 is transferred to the image processing device 5.

As shown in FIG. 1, the image processing device 5 includes: a backgroundluminance estimation unit 7; an expansion parameter setting unit(setting unit) 9; a seed generation unit 11; a region generation unit(cell region formation unit) 13; and an output unit 15. Each of theseprocessing units is connected to a system controller, not shown in thefigure, so that the operation thereof is controlled. In addition, eachof these processing units may be composed of, for example, a centralprocessing unit (CPU) and a storage device, such as a random accessmemory (RAM) or a read only memory (ROM), for storing an arithmeticoperation program. In this case, it is advisable that the ROM store acell recognition program serving as the arithmetic operation program.

For the image of the raw image signal sent from the image acquisitiondevice 3, the background luminance estimation unit 7 estimates the meanluminance value of the pixels (corresponding to voxels in the case of athree-dimensional image) belonging to a background region, which is anon-spheroid region, as shown in FIG. 2, not including the spheroid S,cells C, and so forth to be recognized.

More specifically, the background luminance estimation unit 7 firstgenerates a luminance histogram, as shown in FIG. 4, on the basis of theluminance value of each pixel from the raw image signal. In FIG. 4, thehorizontal axis indicates the luminance, and the vertical axis indicatesthe pixel frequency. The luminance values of the pixels belonging to thebackground are likely to be localized in a certain luminance width, andthe histogram can be regarded as a mixture distribution in which a sharpnormal distribution A, composed of pixels belonging to the background,and a flat normal distribution B, composed of pixels belonging to thespheroid S, are mixed.

For this reason, as shown in FIG. 4, the background luminance estimationunit 7 performs fitting of the luminance histogram using known fittingprocessing based on the weighted addition of the two normaldistributions A and B, namely, a mixture normal distribution. As theknown fitting processing, any processing, such as theexpectation-maximization algorithm (EM algorithm), can be applied.

In addition, the background luminance estimation unit 7 is configured toestimate, as the background luminance, the parameter mean value in theapparently sharper normal distribution (normal distribution A in FIG. 4)of the fitted bimodal mixture normal distribution. The backgroundluminance value estimated by the background luminance estimation unit 7is transferred to the seed generation unit 11.

The expansion parameter setting unit 9 allows a user to enter and set anapparent Z-direction expansion rate for the morphologies of the cells Cin the cell image.

In the seed generation unit 11, information about a group of coordinatesof potential cell positions, which are highly likely to correspond tothe center positions of the individual cells C, in the raw image signalacquired by the image acquisition device 3 is generated as seedinformation. As shown in FIG. 5, this seed generation unit 11 includes:an initialization unit 17; a LoG filter unit (feature value calculationunit) 19; a seed detection processing unit (peak position detectionunit) 21; a seed integration processing unit 23; and a sorting unit 25.

The initialization unit 17 reads the raw image signal sent from theimage acquisition device 3 and initializes (normalizes) the luminancevalue of each pixel to a 12-bit range. In other words, theinitialization unit 17 adjusts the gradation width to the range of 0 to4095. More specifically, the initialization unit 17 obtains a maximumpixel value max in the raw image signal and then multiplies each pixelvalue by Gain (=4095/max). The raw image signal initialized by theinitialization unit 17 is transferred to the LoG filter unit 19.

The LoG filter unit 19 applies a Laplacian-of-Gaussian filter (LoGfilter) to the raw image signal initialized by the initialization unit17. The LoG filter is represented as a filter having the effects of aGaussian filter (smoothing filter) and a Laplacian filter (quadraticdifferential (edge detection) filter) combined.

An example of a two-dimensional LoG filter is shown below.LoG(x,y)=(x ² +y ²−2σ²)/(2πσ⁶)×exp{−(x ² +y ²)/(2σ²)}

Here, x and y represent a pixel position, LoG(x, y) represents a filteroutput value (feature value, referred to hereinafter as a LoG outputvalue), and σ represents a parameter for adjusting the filter effect.

In the cell image, the cells C are often represented as “bright spots”having a higher luminance than the peripheries thereof, and nowadays,the LoG filter is often used to detect the luminance peak position=cellposition. The LoG filter unit 19 applies the two-dimensional LoG filterto the (two-dimensional) slice plane image at each Z position of theinitialized raw image signal, obtains an LoG output value (featurevalue) for each pixel, which represents how likely the pixel value is tobe an extreme value, and generates an LoG output value image in whichthese values are recorded as pixel values. The LoG output value imagegenerated by the LoG filter unit 19 is transferred to the seed detectionprocessing unit 21.

Here, the two-dimensional LoG filter is applied to each of the Z sliceplane images. Instead of this, the LoG output value may be obtained byapplying a three-dimensional LoG filter directly to each pixel of thethree-dimensional Z-stack image. In this case, due to, for example, lowresolution in the Z direction, depending on an optical system condition,such as the point spread function (PSF), of the image acquisition device3 or the state of the cell cluster serving as a subject, the individualcells C may appear to be expanded in the Z direction in the acquiredimage, as shown in, for example, FIG. 6.

As a measure against this, the LoG filter unit 19 may be adjusted on thebasis of the Z-direction expansion rate set by the user in the expansionparameter setting unit 9 so that the LoG filter is applied more stronglyin the Z direction. In addition, the LoG filter unit 19 may calculateLoG output values from pixel values included in a region in a prescribedrange that has been set on the basis of an optical system condition,such as the PSF, of the image acquisition device 3. By doing so, in acase where, for example, the individual cells appear to be expanded as aresult of blurring taking place in the cell image due to image qualitydeterioration, such as low optical performance, during imageacquisition, the LoG output values can be calculated with this expansionbeing taken into account.

On the basis of the LoG output value image sent from the LoG filter unit19, the seed detection processing unit 21 detects peak pixel positions(peak positions) indicating local maximum LoG output values. Morespecifically, the seed detection processing unit 21 two-dimensionallyscans the (two-dimensional) slice plane image at each Z position of theLoG output value image and detects a peak pixel position the LoG outputvalue of which is higher than the values of peripheral pixels(prescribed feature value threshold value) neighboring the peak pixelposition. Thereafter, the seed detection processing unit 21 outputs, asthe seed information, position coordinate information (seed coordinatesrepresented with XZY coordinate values) of the detected peak pixelposition and the LoG output value at that peak pixel position.

In addition, just in case minute luminance irregularities in thebackground may be erroneously detected as seeds (peak positions), theseed detection processing unit 21 applies threshold value processing tothe luminance value of the pixel, on the raw image signal, correspondingto each set of the seed coordinates by using a pixel threshold valuethat has been set for luminance on the basis of the background luminancevalue estimated by the background luminance estimation unit 7, therebydeleting seed information if the luminance value thereof is equal to orless than a certain luminance value. By doing so, noise that has beenerroneously detected as peak pixel positions can be eliminated. The seedinformation detected by the seed detection processing unit 21 istransferred to the seed integration processing unit 23. Here, the peakpixel positions are obtained two-dimensionally. Instead of this,three-dimensional peak pixel positions, including positions in the Zdirection, may be obtained.

Regarding the seed coordinates included in the plurality of items ofseed information sent from the seed detection processing unit 21, theseed integration processing unit 23 integrates any two sets of seedcoordinates that are very close to each other with respect to the meansize of the cells C.

More specifically, the seed integration processing unit 23 first takesout the sets of seed coordinates from two items of seed information andcalculates the spatial distance between the sets of seed coordinates.The seed integration processing unit 23 then uses a proximity thresholdvalue that is preset on the basis of the mean cell size (cell radius)and integrates the two sets of seed coordinates the spatial distancebetween which is equal to or less than the proximity threshold value, inother words, the two sets of seed coordinates determined as sets ofcoordinates of two seeds (peak positions) that are sufficientlyproximate to each other compared with the cell size.

The integration of seed coordinates is performed such that the LoGoutput value is compared between the two peak pixel positionscorresponding to two neighboring sets of seed coordinates, and the setof seed coordinates having a larger LoG output value are kept, whereasthe other set of seed coordinates are discarded, assuming that the peakpixel position having the larger LoG output value is more likely to be aseed (closer to the center position of a cell C). The seed integrationprocessing unit 23 exhaustively searches all items of seed informationfor adjacent sets of seed coordinates, repeats this processing untilthere are no other sets of seed coordinates to be integrated, andtransfers the kept seed information to the sorting unit 25.

The sorting unit 25 refers to each of the LoG output values in the seedinformation sent from the seed integration processing unit 23 andarranges (sorts) these items of seed information in descending order ofthe LoG output value. The larger the LoG output value, the more likelythat the LoG output value indicates a cell C, and hence, by doing so,cell regions can be sequentially formed in a more efficient manner,starting with the seed information having the largest LoG output value.The seed information sorted by the sorting unit 25 is transferred to theregion generation unit 13.

In the region generation unit 13, each set of the seed coordinates isselected on the basis of the seed information generated by the seedgeneration unit 11 in the order that the seed coordinates are sorted indescending order of the LoG output value, and individual cell regionsare formed for all the selected sets of seed coordinates on the basis ofthe distribution of the luminance values of pixels peripheral to each ofthe sets of seed coordinates.

As shown in FIG. 7, this region generation unit 13 includes a seedrecording unit (peak position recording unit) 27, a region recordingunit 29, a position recording unit 31, a seed selection unit 33, a firstoverlapping determination unit (proximity state determination unit) 35,a cell position correction unit (first correction unit) 37, a firstregion formation unit (cell region formation unit) 39, a first regionposition setting unit (cell region position identification unit) 41, aninter-region position distance calculation unit 43, a threshold valueprocessing unit (proximity state determination unit) 45, a regionintegration processing unit (cell region formation unit, secondcorrection unit) 47, a second region position setting unit (cell regionposition identification unit) 49, a second overlapping determinationunit (proximity state determination unit) 51, a second region formationunit (cell region formation unit, third correction unit) 53, and a thirdregion position setting unit (cell region position identification unit)55.

The seed recording unit 27 functions as a buffer memory for temporarilyrecording the seed information sent from the seed generation unit 11. Inthe seed recording unit 27, seed information that is recorded in aseries of region generation steps is updated as needed. In addition, theseed recording unit 27 deletes recorded seed information according tothe determination result from the first overlapping determination unit35.

The region recording unit 29 functions as a buffer memory of the samesize as that of the raw image signal, the buffer memory being used torecord interim results of region generation during a series ofprocedures for generating cell regions for individual sets of seedcoordinates. In the region recording unit 29, the generated cell regionsare recorded in the buffer and updated, as needed. In addition, whenregion generation for all sets of seed coordinates is finished, theregion recording unit 29 records a cell region division image in whichindividual cell regions are delimited, and then the result of the cellregion division is transferred to the output unit 15.

The position recording unit 31 functions as a buffer memory fortemporarily recording the center position of each of the cell regionsrecorded in the region recording unit 29. In the position recording unit31, the cell regions recorded in a series of region generation steps areupdated as needed.

The seed selection unit 33 selects sets of seed coordinates included inthe seed information recorded in the seed recording unit 27, one at atime, in the sorted order and transfers them to the first overlappingdetermination unit 35.

On the basis of the seed coordinates of the currently formed cell regionthat are sent by the seed selection unit 33 and information about apreviously formed cell region recorded in the region recording unit 29,the first overlapping determination unit 35 determines whether or notthere is an overlapping cell region, as shown in FIG. 8A. Morespecifically, the first overlapping determination unit 35 determineswhether or not the distance between the set of seed coordinates sent bythe seed selection unit 33 and the set of seed coordinates of the cellregion recorded in the region recording unit 29 is equal to or less thana prescribed peak threshold value.

When the first overlapping determination unit 35 determines that thisdistance is equal to or less than the prescribed peak threshold value,the information about the seed coordinates of the currently formed cellregion is sent to the seed recording unit 27, and furthermore theinformation about the seed coordinates, as well as the information aboutthe previously formed overlapping cell region, are sent to the cellposition correction unit 37. In this case, in the seed recording unit27, the seed information corresponding to the information about seedcoordinates that has been sent from the first overlapping determinationunit 35 is deleted from the record. By doing so, it is not necessary toform a cell region redundantly for a set of seed coordinates adjacent toa set of seed coordinates for which a cell region has already formed,and furthermore, the seed recording unit 27 does not need to redundantlyrecord such seed information.

On the other hand, when the first overlapping determination unit 35determines that the distance is larger than the prescribed peakthreshold value (does not overlap), the information about the seedcoordinates of that currently formed cell region is transferred to thefirst region formation unit 39.

On the basis of information about the seed coordinates sent from thefirst overlapping determination unit 35 and information about thepreviously formed overlapping cell region, the cell position correctionunit 37 searches for the center position of the previously formedoverlapping cell region, said center position being recorded in theposition recording unit 31.

Thereafter, the cell position correction unit 37 corrects the seedcoordinate values of the previously formed overlapping cell region, saidseed coordinate values being recorded in the position recording unit 31,by shifting, by a prescribed amount, the found center position towardsthose seed coordinates sent from the first overlapping determinationunit 35, and ends processing on the seed (seed information) for which aregion is currently formed. For example, when the set of coordinates ofthe center position before correction is referred to as Pold and the setof seed coordinates is referred to as Pseed, the mid coordinates(Pold+Pseed)/2 may be assigned to the coordinates of the centerposition, after correction, as Pnew.

The first region formation unit 39 forms a cell region on the basis of aspatial distribution of pixel values peripheral to the set of seedcoordinates sent from the first overlapping determination unit 35. Morespecifically, in response to the raw image signal sent from the imageacquisition device 3, the first region formation unit 39 first trims aregion in a prescribed range centered on the set of seed coordinatessent from the first overlapping determination unit 35. The size of thetrimming region is specified on the basis of the above-described cellradius.

In this case, due to, for example, low resolution in the Z direction,depending on an optical system condition, such as the point spreadfunction (PSF), of the image acquisition device 3 or the state of thecell cluster serving as a subject, the individual cells C may appear tobe expanded in the Z direction in the acquired image, as shown in FIG.6. In that case, the first region formation unit 39 adjusts the trimmingrange so as to be expanded in the Z direction on the basis of theZ-direction expansion rate entered by the user in the expansionparameter setting unit 9.

In addition, the first region formation unit 39 generates a smoothedregion the border of which is smoothed by applying a smoothing filter tothe trimming region. Thereafter, the first region formation unit 39obtains edge strengths, defined by luminance gradient, of pixels thatare away from the center of the smoothed region by more than the cellradius and that are in the smoothed region and performs the correctionprocess of replacing the pixel values of pixels having an edge strengthlower than a prescribed threshold value with the above-describedbackground luminance value.

Here, the luminance gradient is calculated from the inner product PV·GVbetween a direction vector PV, obtained by normalizing the positionvector of a pixel with the origin adjusted to the center of the smoothedregion, and a gradient vector GV calculated from the difference(gradient) in luminance value from the neighboring pixel. In addition,the magnitude of the edge strength threshold value for a pixel may beadjusted according to the distance from the center of the smoothedregion to the pixel, like threshold value THedge=0 (distance<cell size),THedge=coef×(Dist−cell size) (distance≥cell size), where coef is aprescribed constant.

The first region formation unit 39 forms a cell region by binarizing thesmoothed region, in which the pixel values have been corrected asdescribed above (correction based on weighted edge strength on eachpixel value), with reference to an adaptive threshold value (binarizedthreshold value) based on the distribution of the pixel values in theregion. The adaptive threshold value may be reset if it is smaller, by aprescribed amount or more, than the maximum value of the pixel values ofa plurality of pixels included in a region peripheral to the peak pixelposition. By doing so, the adaptivity of the adaptive threshold value isenhanced, thereby making it possible to form a cell region with higheraccuracy. In addition, the first region formation unit 39 may set apixel range peripheral to the set of seed coordinates on the basis ofthe LoG output value of the peak pixel position.

In addition, just in case there is a hole in the cell region formed viabinarization, the first region formation unit 39 also functions as ahole filling processing unit for filling the hole in the cell region byapplying hole filling processing. Hole filling processing can make thedistribution of the cell regions easier to understand. In addition, thefirst region formation unit 39 also functions as a fragment-regionremoving unit that, if a cell region fragmented into a plurality ofregions is formed, keeps the region closest to the peak pixel positionand discards regions not including the center position. This isnecessary for a case where fragmented regions of a neighboring cell Cmay be erroneously detected as cell regions at positions away from thecenter during binarization. The cell region formed by the first regionformation unit 39 is transferred to the first region position settingunit 41, the region integration processing unit 47, and the secondoverlapping determination unit 51.

The first region position setting unit 41 calculates the center positionof the cell region sent from the first region formation unit 39, setsthe calculated center position as the region position, and transfers itto the inter-region position distance calculation unit 43 and the secondoverlapping determination unit 51.

The inter-region position distance calculation unit 43 refers to thecenter position (region position) of a previously formed cell regionrecorded in the position recording unit 31, calculates the distance fromthe center position of the previously formed cell region to the regionposition (center position of the cell region) sent from the first regionposition setting unit 41, and transfers it to the threshold valueprocessing unit 45.

The threshold value processing unit 45 compares the distance calculatedby the inter-region position distance calculation unit 43 with aprescribed inter-center position threshold value and transfers thecomparison result to the region integration processing unit 47 and thesecond overlapping determination unit 51. The prescribed inter-centerposition threshold value needs to be larger than the prescribed peakposition threshold value and is set on the basis of, for example, theLoG output value of the corresponding peak pixel position in the cellregion.

When the threshold value processing unit 45 determines that the distanceis equal to or less than the prescribed inter-center position thresholdvalue, the region integration processing unit 47 integrates these cellregions into one cell region. This can prevent delimitation of cellregions from becoming too complicated and difficult to understandbecause cell regions whose respective sets of seed coordinates are awayfrom each other by more than the prescribed peak threshold value butthat are so close to each other that the distance between the respectiveregion positions is equal to or less than the prescribed inter-centerposition threshold value are difficult to delimit and, in some cases, alarge portion of one cell region is hidden by another cell region.

More specifically, as shown in FIG. 8B, the region integrationprocessing unit 47 forms a new cell region by integrating the cellregion formed by the first region formation unit 39 with an existingproximate cell region. The cell region formed by the region integrationprocessing unit 47 is transferred to the region recording unit 29 andthe second region position setting unit 49, and processing on the seed(seed information) ends.

The second region position setting unit 49 calculates the centerposition of the cell region sent from the region integration processingunit 47, sets the calculated center position as the region position, andtransfers it to the position recording unit 31.

When the threshold value processing unit 45 determines that the distanceis greater than the prescribed inter-center position threshold value,the second overlapping determination unit 51 determines whether or not apreviously formed cell region recorded in the region recording unit 29overlaps the region position (center position of the cell region) sentfrom the first region position setting unit 41.

When the second overlapping determination unit 51 determines that theregion position sent from the first region position setting unit 41 isnot disposed so as to overlap the previously formed cell region, thecell region at that region position sent from the first region formationunit 39 is transferred to and recorded in the region recording unit 29,that region position is transferred to and recorded in the positionrecording unit 31, and processing on that seed (seed information) ends.

On the other hand, when the second overlapping determination unit 51determines that the region position sent from the first region positionsetting unit 41 is disposed so as to overlap the previously formed cellregion, the cell region at that region position sent from the firstregion formation unit 39 and the previously formed overlapping cellregion are transferred to the second region formation unit 53.

As shown in FIG. 8C, the second region formation unit 53 re-forms thetwo cell regions so as to be restricted to the region occupied by thetwo overlapping cell regions sent from the second overlappingdetermination unit 51. Cell regions in which the respective sets of seedcoordinates are away from each other by more than the prescribed peakthreshold value and the region positions are also away from each otherby more than the prescribed inter-center position threshold value but inwhich the region position of one cell region overlaps the other cellregion have a sufficiently large non-overlapping region, and hence suchcell regions neighboring each other can be displayed so as to bedelimited in the manner as described above. Although a known regiondivision method may be used to re-form the cell region, a regiondivision method based on the relatively convenient region growing methodis applied in this example to re-form the cell region.

More specifically, the second region formation unit 53 arranges, in theregion occupied by the two cell regions, new initial regions of aprescribed size, representing two cells C, applies the region growingmethod to each of the initial regions to allow each of the regions togrow, and splits the region into two cell regions again, thus re-formingeach of the cell regions. The two initial regions are spherical regionsthe sizes of which are adjusted so that the regions do not overlap eachother. The two cell regions re-formed by the second region formationunit 53 are transferred to and recorded in the region recording unit 29and are also transferred to the third region position setting unit 55.

The third region position setting unit 55 calculates the center positionof each of the two re-formed cell regions sent from the second regionformation unit 53, sets each of the calculated center positions as thecorresponding region position, and transfers it to the positionrecording unit 31.

The output unit 15 outputs the final result of the cell region division.

Next, the cell recognition method according to this embodiment will bedescribed.

As illustrated in the flowcharts of FIGS. 9 and 10, the cell recognitionmethod according to this embodiment includes: an LoG filter processingstep (feature value calculation step) SA3 of calculating an LoG outputvalue (feature value) representing how likely the pixel value in eachpixel is to be an extreme value in the cell image acquired by capturingan image of the cell cluster composed of the plurality of cells C; aseed detection processing step (peak position recording step) SA5 ofdetecting and recording a peak pixel position at which the LoG outputvalue is larger than a prescribed feature value threshold value in thecell image; a cell region formation step SA8 of forming, for each peakpixel position recorded in the seed detection processing step SA5, acell region on the basis of the distribution of the pixel values of aplurality of pixels included in a region peripheral to the peak pixelposition in the cell image; a first region position setting processingstep SB5, a second region position setting processing step SB9, and athird region position setting processing step SB12 serving as a cellregion position identification step of identifying the center positionof each cell region formed in the cell region formation step SA8; andproximity state determination steps SB1, SB7, and SB10 of determining aproximity state between the cell region being currently formed in thecell region formation step SA8 and a previously formed cell region byusing at least one of the peak pixel position, the morphology of thecell region, and the center position of the cell region.

Thereafter, when it is determined in the proximity state determinationsteps SB1, SB7, and SB10 that the proximity state described above issatisfied, at least one of the currently formed cell region and thepreviously formed cell region is corrected in the cell region formationstep SA8.

The operation of the image processing device 5, the cell recognitiondevice 1, and the cell recognition method with the above-describedstructure will be described below.

As illustrated in the flowchart of FIG. 9, in order to recognize thecells C using the image processing device 5, the cell recognition device1, and the cell recognition method according to this embodiment,three-dimensional Z-stack image data acquired by the image acquisitiondevice 3 is first input to the background luminance estimation unit 7,the seed generation unit 11, and the region generation unit 13 as a rawimage signal from which cells are recognized (step SA1).

Next, in the seed generation unit 11, the input raw image signal isinitialized by the initialization unit 17 (step SA2), and then the LoGfilter is applied by the LoG filter unit 19 to that initialized rawimage signal (Log filter processing, step SA3). Thereafter, an LoGoutput value is calculated by the LoG filter unit 19 for each pixel ofthe initialized raw image signal to generate an LoG output value image,and the generated LoG output value image is transferred to the seeddetection processing unit 21.

Furthermore, a luminance histogram, as shown in FIG. 4, is generated bythe background luminance estimation unit 7 from the input raw imagesignal, and a background luminance value is estimated on the basis ofthis generated luminance histogram (background luminance valueestimation processing, step SA4).

Subsequently, on the basis of the LoG output value image sent from theseed generation unit 11, peak pixel positions at which the LoG outputvalues indicate maximum values locally, as shown in FIG. 11, aredetected by the seed detection processing unit 21 as potential cellpositions (seed detection processing, step SA5). In FIG. 11, referencesign P denotes a peak pixel position. Thereafter, the sets of seedcoordinates of the detected peak pixel positions and the LoG outputvalues at the peak pixel positions are transferred as seed informationfrom the seed detection processing unit 21 to the seed integrationprocessing unit 23.

Subsequently, the distance between the sets of seed coordinates in twoitems of seed information is calculated and it is determined whether ornot the distance is equal to or less than the proximity threshold valueby the seed integration processing unit 23. Thereafter, when it isdetermined that the distance is equal to or less than the proximitythreshold value, the set of seed coordinates having the larger LoGoutput value of the two sets of seed coordinates are kept, and the otherset of seed coordinates are discarded (seed integration processing, stepSA6). This integration processing is applied to all items of seedinformation, and the kept seed information is transferred to the sortingunit 25. Thereafter, the seed information is sorted by the sorting unit25 in descending order of the LoG output value and is then transferredto the region generation unit 13.

Subsequently, in the region generation unit 13, the seed informationsent from the sorting unit 25 is first recorded by the seed recordingunit 27. As shown in FIG. 12, the seed information recorded in the seedrecording unit 27 is then selected by the seed selection unit 33, one ata time, in descending order of the LoG output value 1 (step SA7).

Next, region formation processing (step SA8) performed by the regiongeneration unit 13 will be described in detail with reference to theflowchart in FIG. 10.

First, the seed information selected by the seed selection unit 33 istransferred to the first overlapping determination unit 35. Then, it isdetermined by the first overlapping determination unit 35 whether or notthe set of seed coordinates of the currently formed cell region sent bythe seed selection unit 33 overlap a previously formed cell regionrecorded in the region recording unit 29. More specifically as shown inFIG. 8A, it is determined by the first overlapping determination unit 35whether or not the distance between the set of seed coordinates sent bythe seed selection unit 33 and the set of seed coordinates of the cellregion recorded in the region recording unit 29 is equal to or less thana prescribed peak threshold value (step SB1).

When it is determined by the first overlapping determination unit 35that the set of seed coordinates of the currently formed cell regionoverlap the previously formed cell region (“YES” in step SB1), the setof seed coordinates of the currently formed cell region are deleted fromthe record by the seed recording unit 27 (seed deletion processing, stepSB2). In addition, the set of seed coordinate values of the previouslyformed cell region are corrected by the cell position correction unit 37on the basis of the set of seed coordinates of the currently formed cellregion (cell position correction processing, step SB3).

On the other hand, when it is determined by the first overlappingdetermination unit 35 that the set of seed coordinates of the currentlyformed cell region do not overlap the previously formed cell region(“NO” in step SB1), a cell region is formed, as shown in FIG. 13, by thefirst region formation unit 39 on the basis of the raw image signal sentfrom the image acquisition device 3 and the spatial distribution ofpixel values peripheral to the set of seed coordinates sent from thefirst overlapping determination unit 35 (first region formationprocessing, step SB4). In FIG. 13, reference sign R denotes a cellregion.

Next, the center position of the cell region formed by the first regionformation unit 39 is calculated by the first region position settingunit 41 and is set as a region position (first region position settingprocessing, cell region position identification step, step SB5), and theset region position is transferred to the inter-region position distancecalculation unit 43 and the second overlapping determination unit 51.

Next, the distance between the center position (region position) of thepreviously formed cell region recorded in the position recording unit 31and the region position (center position of the cell region) sent fromthe first region position setting unit 41 is calculated by theinter-region position distance calculation unit 43 (inter-regionposition distance calculation processing, step SB6), and the calculateddistance is transferred to the threshold value processing unit 45.

Next, it is determined by the threshold value processing unit 45 whetheror not the distance calculated by the inter-region position distancecalculation unit 43 is equal to or less than a preset prescribedinter-center position threshold value (step SB7).

When it is determined by the threshold value processing unit 45 that thedistance is equal to or less than the prescribed inter-center positionthreshold value, the cell region formed by the first region formationunit 39 is integrated with the existing, proximate cell region by theregion integration processing unit 47, as shown in FIG. 8B, thus forminga new cell region (region integration processing, step SB8).

Thereafter, the new cell region formed by the region integrationprocessing unit 47 is recorded by the region recording unit 29, thecenter position of the new cell region is calculated by the secondregion position setting unit 49 and is set as a region position (secondregion position setting processing, step SB9), and the set regionposition is recorded by the position recording unit 31.

On the other hand, when it is determined by the threshold valueprocessing unit 45 that the distance is greater than the prescribedinter-center position threshold value, it is determined by the secondoverlapping determination unit 51 whether or not the previously formedcell region recorded in the region recording unit 29 overlaps the regionposition (center position of the cell region) sent from the first regionposition setting unit 41 (step SB10).

When it is determined by the second overlapping determination unit 51that the previously formed cell region overlaps the region position sentfrom the first region position setting unit 41, the two cell regions arere-formed by the second region formation unit 53, as shown in FIG. 8C,such that the two cell regions are restricted to the region occupied bythe two overlapping cell regions sent from the second overlappingdetermination unit 51 (second region formation processing, step SB11).

Thereafter, the two cell regions re-formed by the second regionformation unit 53 are recorded by the region recording unit 29, thecenter positions of the two cell regions are calculated by the thirdregion position setting unit 55 and are set as the respective regionpositions (third region position setting processing, step SB12), andeach of the set region positions is recorded by the position recordingunit 31.

On the other hand, when it is determined by the second overlappingdetermination unit 51 that the previously formed cell region does notoverlap the region position sent from the first region position settingunit 41, the cell region at that region position is recorded in theregion recording unit 29, and that region position is recorded in theposition recording unit 31, thus ending processing on the present seed(seed information).

Finally, it is determined by the system controller (not shown in thefigure) whether or not region formation is completed for all seeds(items of seed information) (step SA9), and if region formation is notcompleted for all seeds, the flow returns to step SA7. On the otherhand, if region formation is completed for all seeds, the processingends, and thereafter, a cell region division image in which individualcell regions are delimited, as shown in FIG. 14, is recorded by theregion recording unit 29, and the result of the cell region division istransferred to the output unit 15 and output.

As described above, according to the image processing device 5, the cellrecognition device 1, and the cell recognition method of thisembodiment, because the cells C often have a higher pixel value than thebackground in the cell image, the cells C are highly likely to bepresent at peak pixel positions at which the LoG output values, whichindicate how likely the pixel values are to be extreme values,calculated by the LoG filter unit 19 are larger than the prescribedfeature value threshold value. Therefore, the region of each of theplurality of cells C constituting the cell cluster can be extracted bycausing the seed detection processing unit 21 to detect a plurality ofpeak pixel positions in the cell image and then causing the regiongeneration unit 13 to form a cell region for each of the sets of seedcoordinates of the peak pixel positions on the basis of the distributionof the pixel values in a region peripheral to the set of the seedcoordinates.

In this case, cell regions are sequentially formed independently foreach of the sets of seed coordinates, and therefore, depending on theorder of region generation, some sets of seed coordinates may alreadyhave cell regions formed thereon on the basis of a previously selectedset of seed coordinates. With this being the situation, if the firstoverlapping determination unit 35, the threshold value processing unit45, and the second overlapping determination unit 51 determine that thecurrently formed cell region is proximate to a previously formed cellregion by using at least one of the peak pixel position, the morphologyof the cell region, and the center position of the cell region, theneach of the neighboring cell regions can be delimited, regardless of apartial overlapping of those cell regions, by causing the first regionformation unit 39, the region integration processing unit 47, and thesecond region formation unit 53 to correct either or both of thecurrently formed cell region and the previously formed cell region. Forthis reason, the individual cells C can be extracted with high accuracyfrom the cell image acquired by capturing an image of the cell clustercomposed of the plurality of living cells C.

In this embodiment, the second overlapping determination unit 51determines whether or not a previously formed cell region recorded inthe region recording unit 29 overlaps the region position (centerposition of the cell region) sent from the first region position settingunit 41. Instead of this, the second overlapping determination unit 51may determine whether or not the region position of a previously formedcell region recorded in the region recording unit 29 overlaps the cellregion that corresponds to the region position sent from the firstregion position setting unit 41 and that is sent from the first regionformation unit 39.

In addition, in this embodiment, the formation of a cell regionperformed by the first region formation unit 39 and the identificationof a region position performed by the first region position setting unit41 may be concurrently applied to pixel positions that are differentfrom each other. Likewise, the formation of a cell region performed bythe region integration processing unit 47 and the identification of aregion position performed by the second region position setting unit 49may be concurrently applied to peak pixel positions that are differentfrom each other, and furthermore, the formation of a cell regionperformed by the second region formation unit 53 and the identificationof a region position performed by the third region position setting unit55 may be concurrently applied to peak pixel positions that aredifferent from each other.

By doing so, formation of a cell region and identification of a regionposition can be performed more efficiently and in a shorter time periodfor all peak pixel positions.

Second Embodiment

Next, an image processing device, a cell recognition device, and a cellrecognition method according to a second embodiment of the presentinvention will be described.

The image processing device, the cell recognition device, and the cellrecognition method according to this embodiment differ from the firstembodiment in that, as shown in FIG. 15, the region of each of the cellsC is divided into the region corresponding to a cell nucleus N (firstcell region) and the region corresponding to a cytoplasm K peripheral tothe cell nucleus N (second cell region) before a cell region is formed.

Hereinafter, the same components in this embodiment as those used in theimage processing device 5, the cell recognition device 1, and the cellrecognition method according to the first embodiment are denoted by thesame reference signs, and thus will not be described.

As shown in FIG. 16, in a cell recognition device 61 according to thisembodiment, the image processing device 5 includes the backgroundluminance estimation unit 7, the expansion parameter setting unit 9, theseed generation unit 11, a region generation unit 63 instead of theregion generation unit 13, the output unit 15, and a newly added regiongrowing unit 65.

Each of these processing units is connected to the system controller(not shown in the figure), so that the operation thereof is controlled.In addition, each of these processing units may be composed of, forexample, a CPU and a storage device, such as a RAM and a ROM, forstoring an arithmetic operation program. In that case, it is advisablethat a cell recognition program, serving as the arithmetic operationprogram, be stored in the ROM.

In the region generation unit 63, individual regions corresponding tothe cell nuclei N and individual regions corresponding to the cytoplasmsK are generated from the raw image signal of the spheroid S, as shown inFIG. 17A, on the basis of the seed information generated by the seedgeneration unit 11.

As shown in FIG. 18, this region generation unit 63 includes: the seedrecording unit (peak position recording unit) 27; the region recordingunit 29; the position recording unit 31; the seed selection unit 33; thefirst overlapping determination unit (proximity state determinationunit) 35; the cell position correction unit (first correction unit) 37;a first region formation unit (cell region formation unit, first regionformation unit, second region formation unit) 67 instead of the firstregion formation unit 39; the first region position setting unit (cellregion position identification unit) 41; the inter-region positiondistance calculation unit 43; the threshold value processing unit(proximity state determination unit) 45; the region integrationprocessing unit (cell region formation unit, second correction unit) 47;the second region position setting unit (cell region positionidentification unit) 49; the second overlapping determination unit(proximity state determination unit) 51; the second region formationunit (cell region formation unit, third correction unit) 53; the thirdregion position setting unit (cell region position identification unit)55; and a newly added cytoplasm region recording unit 69.

The first region formation unit 67 forms a region corresponding to thecell nucleus N (hereinafter, referred to as the cell nucleus region), asshown in FIG. 17B, via the same processing as in the first regionformation unit 39 of the first embodiment, on the basis of the spatialdistribution of pixel values peripheral to the set of seed coordinatessent from the first overlapping determination unit 35 such that the cellnucleus region is restricted to a region that is proximate to the set ofseed coordinates and that has a higher luminance than a prescribedluminance threshold value. The cell nucleus region formed by the firstregion formation unit 67 is transferred to the first region positionsetting unit 41, the region integration processing unit 47, and thesecond overlapping determination unit 51.

In addition, concurrently with the formation of the cell nucleus region,the first region formation unit 67 forms, on the basis of the spatialdistribution of pixel values peripheral to the set of seed coordinates,a region corresponding to the cytoplasm K (hereinafter, referred to asthe cytoplasm region), as shown in FIG. 17C, including a region having aluminance equal to or less than the prescribed luminance threshold valuesuch that the region is not restricted to the proximity of the set ofseed coordinates.

More specifically, the first region formation unit 67 forms a cytoplasmregion by applying binarization processing based on adaptive thresholdvalue processing directly to a trimming region image centered on the setof seed coordinates sent from the first overlapping determination unit35. The cytoplasm region formed by the first region formation unit 67 istransferred to the cytoplasm region recording unit 69.

In this embodiment, the first region position setting unit 41 calculatesthe center position of the cell nucleus region sent from the firstregion formation unit 67, sets it as the region position, and transfersthe region position to the inter-region position distance calculationunit 43 and the second overlapping determination unit 51.

The cytoplasm region recording unit 69 functions as a buffer memory ofthe same size as that of the raw image signal, the buffer memory beingused to record an interim result in a series of procedures forgenerating a cytoplasm region for each set of seed coordinates. In thecytoplasm region recording unit 69, the formed cytoplasm region iswritten to the buffer and is updated, as needed.

In the final stage, a cytoplasm region image, representing thedistribution of the cytoplasms, is recorded in the cytoplasm regionrecording unit 69. When the cytoplasm region image is recorded in thecytoplasm region recording unit 69, individual cytoplasms do not alwaysneed to be delimited in the cytoplasm region image. The cytoplasm regionimage recorded by the cytoplasm region recording unit 69 is transferredto the region growing unit 65.

In this embodiment, in the final stage, a cell nucleus region divisionimage, representing individual cell nucleus regions, is recorded in theregion recording unit 29. The cell nucleus region division imagerecorded by the region recording unit 29 is transferred to the regiongrowing unit 65.

The region growing unit 65 applies a region division method based on theregion growing method to the cell nucleus region division image sentfrom the region recording unit 29 of the region generation unit 63 andto the cytoplasm region image sent from the cytoplasm region recordingunit 69 of the region generation unit 63 and generates a cell regiondivision image in which individual cell regions are delimited.

More specifically, as shown in FIG. 17D, the region growing unit 65places a new individual cell nucleus region (region corresponding to thecell nucleus N), serving as an initial region, in each of the cytoplasmregions (regions corresponding to the cytoplasm K) and subsequentlygrows the initial region by applying the region growing method to theinitial region such that the region growing range is restricted to thecytoplasm region, thus forming individual cell regions as shown in FIG.17E. By doing so, a cell region division image, in which individual cellregions are delimited, is formed. In FIG. 17E, reference sign R denotesa cell region. The cell region division image formed by the regiongrowing unit 65 is transferred to the output unit 15.

Next, the cell recognition method according to this embodiment will bedescribed.

As illustrated in the flowcharts of FIGS. 19 and 20, the cellrecognition method according to this embodiment includes: a cell nucleusregion formation step (first region formation step) SB4′-1 in which acell region formation step SA8′, instead of the cell region formationstep SA8 in the first embodiment, forms a cell nucleus region on thebasis of a luminance distribution such that the cell nucleus region isrestricted to a region that is proximate to the peak pixel position andthat has a higher luminance than the prescribed luminance thresholdvalue; a cytoplasm region formation step (second region formation step)SB4′-2 of forming a cytoplasm region, including a region having aluminance equal to or less than the prescribed luminance thresholdvalue, on the basis of the luminance distribution without beingrestricted to the proximity of the peak position; and a region growingstep SA10 of assigning each of the cell nucleus regions formed for allpeak pixel positions in the cell nucleus region formation step SB4′-1 toan initial region and making the initial region grow such that theregion growing range is restricted to the cytoplasm region formed in thecytoplasm region formation step SB4′-2.

The operation of the image processing device 5, the cell recognitiondevice 61, and the cell recognition method with the above-describedstructure will be described.

As illustrated in the flowchart of FIG. 20, in order to recognize thecells C using the image processing device 5, the cell recognition device61, and the cell recognition method according to this embodiment, whenit is determined by the first overlapping determination unit 35 that theset of seed coordinates of the currently formed cell region sent by theseed selection unit 33 overlap a previously formed cell region recordedin the region recording unit 29 (“YES” in step SB1), the set of seedcoordinates of the currently formed cell nucleus region are deleted fromthe record by the seed recording unit 27 (seed deletion processing, stepSB2), and the set of seed coordinate values of the previously formedcell nucleus region are corrected by the cell position correction unit37 on the basis of the set of seed coordinates of the currently formedcell nucleus region (cell position correction processing, step SB3).

On the other hand, when it is determined by the first overlappingdetermination unit 35 that the set of seed coordinates of the currentlyformed cell region do not overlap the previously formed cell region(“NO” in step SB1), a cell nucleus region restricted to the proximity ofthe set of seed coordinates, as shown in FIG. 17B, is formed by thefirst region formation unit 39 by following the same procedure as in thefirst embodiment (cell nucleus region formation processing, stepSB4′-1).

The cell nucleus region formed by the first region formation unit 67 istransferred to the first region position setting unit 41, the regionintegration processing unit 47, and the second overlapping determinationunit 51. Thereafter, in the same manner as in the processing performedon a cell region in the first embodiment, steps SB5 to SA9 are performedfor the cell nucleus region. Thereafter, the formed cell nucleus regionis transferred to the region recording unit 29, and a cell nucleusregion division image, representing individual cell nucleus regions,that is finally recorded in the region recording unit 29 is transferredto the region growing unit 65.

In addition, concurrently with the formation of the cell nucleus region,a cytoplasm region, as shown in FIG. 17C, including a region peripheralto the set of seed coordinates, without being restricted to theproximity of the set of seed coordinates, is formed by the first regionformation unit 39 (cytoplasm region formation processing, step SB4′-2).The cytoplasm region formed by the first region formation unit 67 istransferred to the cytoplasm region recording unit 69. Thereafter, thecytoplasm region image, which is finally recorded in the cytoplasmregion recording unit 69, is transferred to the region growing unit 65.

Lastly, when it is determined by the system controller (not shown in thefigure) that region formation is completed for all seeds (“YES” in stepSA9), the region growing unit 65 applies a region division method basedon the region growing method to the cell nucleus region division imagesent from the region recording unit 29 and to the cytoplasm region imagesent from the cytoplasm region recording unit 69, thereby formingindividual cell regions as shown in FIG. 17E (region growing processing,step SA10). Thereafter, the result of cell region division in the formof the cell region division image, in which individual cell regions aredelimited, is transferred from the region growing unit 65 to the outputunit 15 and is output.

As described above, each entire cell region is composed of the regioncorresponding to the cell nucleus N and the region corresponding to thecytoplasm K such that the cytoplasm region is disposed in the peripheryof the cell nucleus region. In a typical cell image, cell nucleusregions have a higher luminance and a more distinct outline thanperipheral cytoplasm regions, and thus, cell nucleus regions are easierto discriminate from each other, resulting in higher division accuracy.On the other hand, a cytoplasm region adjoins another cytoplasm region,and it is often difficult to identify the boundary thereof. Thus, it isdifficult to increase the accuracy at which cytoplasm regions areseparated from each other, compared with cell nucleus regions.

According to the image processing device 5, the cell recognition device61, and the cell recognition method of this embodiment, the first regionformation unit 67 forms a cell nucleus region that is proximate to apeak pixel position and that has a higher luminance than the prescribedluminance threshold value, uses the individual cell nucleus region as aninitial region in the region growing method, forms a cytoplasm regionincluding a region having a luminance equal to or less than theprescribed luminance threshold value without being restricted to theproximity of the peak pixel position, and uses the cytoplasm region as alimit for the region growing range, thereby making it possible toenhance the accuracy of the shape of the cell region based on thecytoplasm region while increasing the cell region division accuracy onthe basis of the cell nucleus region.

In this embodiment, the second overlapping determination unit 51 maydetermine whether or not a previously formed cell nucleus regionrecorded in the region recording unit 29 overlaps the region position(center position of the cell nucleus region) sent from the first regionposition setting unit 41 or may determine whether or not the regionposition of a previously formed cell nucleus region recorded in theregion recording unit 29 overlaps the cell nucleus region thatcorresponds to the region position sent from the first region positionsetting unit 41 and that is sent from the first region formation unit67.

Furthermore, in this embodiment, the formation of the cell nucleusregion performed by the first region formation unit 67 and theidentification of the region position performed by the first regionposition setting unit 41 may be performed concurrently for peak pixelpositions that are different from each other. Likewise, the formation ofthe cell nucleus region performed by the region integration processingunit 47 and the identification of the region position performed by thesecond region position setting unit 49 may be performed concurrently forpeak pixel positions that are different from each other, andfurthermore, the formation of the cell nucleus region performed by thesecond region formation unit 53 and the identification of the regionposition performed by the third region position setting unit 55 may beperformed concurrently for peak pixel positions that are different fromeach other.

Moreover, although each of the above-described embodiments has beendescribed by way of an example where the image processing method isrealized with hardware, instead of this, the image processing method maybe realized with a computer-executable image processing program.

Although the embodiments of the present invention have been described indetail with reference to the drawings, the specific structure is notlimited to those of these embodiments but includes design changes etc.that do not depart from the spirit of the present invention. The presentinvention is not limited to the invention applied to each of theabove-described embodiments and modifications but can be applied to, forexample, embodiments in which these embodiments and modifications areappropriately combined, and is not particularly limited.

From the above-described embodiment, the following invention is alsoderived.

A first aspect of the present invention is an image processing deviceincluding: a feature value calculation unit for calculating a featurevalue, the feature value representing how likely a pixel value in eachof pixels in a cell image formed by capturing an image of a cell clustercomposed of a plurality of cells is to be an extreme value; a peakposition detection unit for detecting, as peak positions, pixelpositions the feature value of which are greater than a prescribedfeature value threshold value in the cell image; a peak positionrecording unit for recording the peak positions detected by the peakposition detection unit; a cell region formation unit for forming, oneat a time for the peak positions recorded by the peak position recordingunit, a cell region on the basis of a distribution of the pixel valuesof a plurality of pixels included in a region peripheral to the peakposition in the cell image; a cell region position identification unitfor identifying a center position of the cell region formed by the cellregion formation unit; and a proximity state determination unit fordetermining, by using at least one of the peak position, a morphology ofthe cell region, and the center position of the cell region, a proximitystate between the cell region currently formed by the cell regionformation unit and a previously formed cell region, wherein, when theproximity state determination unit determines that the proximity stateis satisfied, the cell region formation unit corrects at least one ofthe currently formed cell region and the previously formed cell region.

According to this aspect, because the cells often include pixel valueshigher than the pixel value of the background in the cell image, cellsare highly likely to exist at peak positions having a feature value(representing how likely the pixel value is to be an extreme value) thatis calculated by the feature value calculation unit and that is largerthan the prescribed feature value threshold value. Therefore, the regionof each of the plurality of cells constituting the cell cluster can beextracted by causing the peak position detection unit to detect aplurality of peak positions in the cell image, causing the peak positionrecording unit to record those peak positions, and causing the cellregion formation unit to form a cell region for each of the peakpositions on the basis of the distribution of the pixel values in aregion peripheral to the pixel position.

In this case, even if neighboring cell regions partially overlap eachother, the individual cell regions can be delimited by causing the cellregion position identification unit to identify the center positions ofthe cell regions and causing the cell region formation unit to correctat least one of the currently formed cell region and a previously formedcell region when the proximity state determination unit determines thatthe currently formed cell region is proximate to the previously formedcell region by using at least one of the peak position, the morphologyof the cell region, and the center position of the cell region.Therefore, individual cells can be extracted with high accuracy from thecell image formed by capturing an image of the cell cluster composed ofthe plurality of living cells.

In the above-described aspect, the cell region formation unit mayinclude a first correction unit that does not perform the formation ofthe cell region when the proximity state determination unit determinesthat the distance between the peak position of the currently formed cellregion and the peak position of the previously formed cell region isequal to or less than a prescribed peak threshold value, and the peakposition recording unit may delete, from the record, the peak positionof the currently formed cell region for which it has been determined bythe proximity state determination unit that the distance between thepeak position of the currently formed cell region and the peak positionof the previously formed cell region is equal to or less than theprescribed peak threshold value.

In the cell region formation unit, cell regions are sequentially formedindependently for each of the peak positions recorded in the peakposition recording unit, and hence, depending on the procedures forforming cell regions, a cell region may have been formed on the basis ofa previously selected peak position. For this reason, with theabove-described structure, the first formation unit eliminates the needfor redundantly forming a cell region for a peak position adjacent to apeak position for which a cell region has been formed and also relievesthe peak position recording unit from redundantly recording such a peakposition.

In the above-described aspect, when the proximity state determinationunit determines that the distance between the peak position of thecurrently formed cell region and the peak position of the previouslyformed cell region is equal to or less than the prescribed peakthreshold value, the first correction unit may correct the centerposition of the previously formed cell region on the basis of the peakposition of the currently formed cell region.

With this structure, the first correction unit can make a correction soas to take a balance between the position of the previously formed cellregion having a peak position adjacent to that of the currently formedcell region and the position of the currently formed cell region.

In the above-described aspect, the cell region formation unit mayinclude a second correction unit that, when the proximity statedetermination unit determines that the distance between the peakposition of the currently formed cell region and the peak position ofthe previously formed cell region is larger than a prescribed peakthreshold value and that the distance between the center position of thecurrently formed cell region and the center position of the previouslyformed cell region is equal to or less than a prescribed inter-centerposition threshold value that is larger than the prescribed peakthreshold value, forms a new cell region by integrating the two cellregions, and the cell region position identification unit may identify acenter position of the cell region newly formed by the second correctionunit.

It is difficult to delimit cell regions if the peak positions thereofare away from each other by more than the prescribed peak thresholdvalue but the center positions thereof are adjacent to each other withina distance of the prescribed inter-center position threshold value, andfurthermore, one of such cell regions may be largely hidden by theother. Therefore, with this structure, the second correction unit canprevent delimitation of such cell regions from becoming so complicatedthat the cell regions are difficult to recognize.

In the above-described aspect, the inter-center position threshold valuemay be set on the basis of the feature value of the corresponding peakposition in the cell region.

In the above-described aspect, the cell region formation unit mayinclude a third correction unit that, when the proximity statedetermination unit determines that the distance between the peakposition of the currently formed cell region and the peak position ofthe previously formed cell region is greater than a prescribed peakthreshold value and that the distance between the center position of thecurrently formed cell region and the center position of the previouslyformed cell region is greater than a prescribed inter-center positionthreshold value, which is greater than the prescribed peak thresholdvalue, but determines that the center position of the currently formedcell region overlaps the previously formed cell region or that thecurrently formed cell region overlaps the center position of thepreviously formed cell region, forms or re-forms the two cell regions soas not to overlap each other, and the cell region positionidentification unit may identify center positions of the two cellregions formed or re-formed by the third correction unit.

Two cell regions—their peak positions are away from each other by morethan the prescribed peak threshold value, their center positions areaway from each other by more than the prescribed inter-center positionthreshold value, but the center position of one cell region overlaps theother cell region—have a sufficiently large non-overlapping region.Therefore, with this structure, the third correction unit can displaysuch neighboring cell regions in a delimited manner.

In the above-described aspect, the third correction unit may form orre-form the two cell regions by using a region growing method.

With this structure, cell regions can be formed so as not to overlapeach other by using a simple method.

In the above-described aspect, the peak position detection unit maydelete the peak position the pixel value of which is equal to or lessthan a prescribed pixel threshold value.

With this structure, noise detected and recorded as a peak position canbe removed.

In the above-described aspect, the prescribed pixel threshold value maybe set on the basis of a background value estimated from thedistribution of the pixel values of the cell image.

With this structure, peak positions can be selected such that regionsdarker than the background are regarded as non cells.

In the above-described aspect, the background value may be estimatedfrom a bimodal Gaussian distribution fitted to the distribution of thepixel values.

The pixel values of the pixels belonging to the background are likely tobe localized in a certain pixel value width, and the histogram can beregarded as a mixture distribution in which a sharp normal distribution,composed of pixels belonging to the background, and a flat normaldistribution, composed of pixels belonging to the cells, are mixed.Therefore, with this structure, the background value can be easilyestimated.

In the above-described aspect, the peak position recording unit may sortthe recorded peak positions by the feature value and, when the distancebetween two neighboring peak positions of the peak positions is equal toor less than a prescribed proximity threshold value, may delete eitherone of the two neighboring peak positions from the record.

The larger the feature value of a peak position, the more likely thepeak position belongs to a cell. Thus, with this structure, cell regionscan be formed efficiently in descending order of feature value, startingwith the peak position having the largest feature value. In this case,if two neighboring peak positions are extremely adjacent to each otherwith reference to the prescribed proximity threshold value, one of thepeak positions can be deleted to prevent delimitation of the cellregions from becoming complicated, thereby making recognition of thecell regions easier.

In the above-described aspect, the feature value calculation unit maycalculate, as the feature value, a LoG (Laplacian of Gaussian) filteroutput value in response to the cell image.

With this structure, the rise of the luminance can be easily detectedusing the LoG filter and can be calculated as a feature value.

In the above-described aspect, the cell region formation unit may formthe cell region by binarizing the plurality of pixels included in theregion peripheral to the peak position with reference to a pixel valuedistribution threshold value that is set on the basis of thedistribution of the pixel values of the plurality of pixels.

With this structure, cell regions can be formed easily.

In the above-described aspect, the cell region formation unit may smooththe plurality of pixels included in the region peripheral to the peakposition and may binarize the plurality of smoothed pixels.

With this structure, cell regions can be formed after the luminance hasbeen smoothed.

In the above-described aspect, the cell region formation unit may set aprescribed adaptive binarization threshold value on the basis of thedistribution of the pixel values and may binarize the plurality ofpixels on the basis of the binarization threshold value.

With this structure, the cell region formation unit automatically setsthe binarization threshold value, which facilitates the formation of acell region.

In the above-described aspect, the prescribed binarization thresholdvalue may be reset when the prescribed binarization threshold value issmaller, by a prescribed amount or more, than a maximum value of thepixel values of the plurality of pixels included in the regionperipheral to the peak position.

With this structure, a cell region can be formed with higher accuracy byenhancing the adaptivity of the prescribed binarization threshold value.

In the above-described aspect, the cell region formation unit may set arange of the region peripheral to the peak position on the basis of thefeature value of the peak position.

In the above-described aspect, the cell region formation unit mayinclude a hole filling processing unit for filling a hole generated inthe formed cell region.

With this structure, the distribution of cell regions can be recognizedmore easily by correcting a cell region with the hole filling processingunit.

In the above-described aspect, the cell region formation unit may smootha boundary of the cell region.

With this structure, the boundary between cell regions can be smoothed.

In the above-described aspect, the cell region formation unit mayinclude a fragment-region removing unit that, when the cell region is aregion divided into a plurality of cell regions, keeps the cell regionclosest to the corresponding peak position and deletes the other cellregions.

With this structure, if fragment regions, like noise, fragmented bybinarization are generated, the problem of erroneously detecting thosefragment regions as cell regions can be suppressed by thefragment-region removing unit.

In the above-described aspect, formation of the cell region performed bythe cell region formation unit and identification of the center positionperformed by the cell region position identification unit may beperformed concurrently on peak positions different from each other.

With this structure, the formation of a cell region and identificationof the center position of the cell region for all peak positions can beperformed efficiently and in a shorter time period.

In the above-described aspect, the feature value calculation unit maycalculate the feature value from the pixel values included in a regionin a prescribed range set on the basis of a point spread function of anoptical system used to acquire the cell image.

With this structure, in a case where individual cells are seeminglyexpanded as a result of blurring that has taken place in the cell imagedue to image quality deterioration during image acquisition, such as lowoptical performance, the feature value can be calculated by taking thisexpansion into account.

In the above-described aspect, the feature value calculation unit maycalculate the feature value from the pixel values included in a regionin a prescribed range set on the basis of an apparent extension rate inZ direction of morphologies of the cells in the cell image.

With this structure, in a case where individual cells are seeminglyexpanded as a result of blurring that has taken place in the cell imagedue to image quality deterioration during image acquisition, such as lowoptical performance, the feature value can be calculated by taking thisexpansion into account.

In the above-described aspect, the cell region formation unit may set arange of the region peripheral to the peak position on the basis of anapparent extension rate in Z direction of morphologies of the cells inthe cell image.

With this structure, in a case where individual cells are seeminglyexpanded as a result of blurring that has taken place in the cell imagedue to image quality deterioration during image acquisition, such as lowoptical performance, a cell region can be formed by taking thisexpansion into account.

The above-described aspect may further include a setting unit forallowing a user to set the extension rate in Z direction.

With this structure, the size of a region peripheral to the peakposition can be set with an expansion rate desired by the user.

In the above-described aspect, the cell region formation unit mayinclude: a first region formation unit for forming a first cell regionon the basis of a luminance distribution such that the first cell regionis restricted to a region that is proximate to the peak position andthat has a luminance higher than a prescribed luminance threshold value;a second region formation unit for forming, on the basis of theluminance distribution, a second cell region including a region having aluminance equal to or less than the prescribed luminance threshold valuesuch that the second cell region is not restricted to the proximity ofthe peak position; and a region growing unit that sets, as an initialregion, each of the first cell regions formed by the first regionformation unit for all of the peak positions and that causes the initialregion to grow such that a region growing range is restricted to thesecond cell region formed by the second region formation unit.

A cell region is divided into the cell nucleus and the cytoplasm, andthe entire cell region is composed of the cell nucleus region and thecytoplasm region disposed peripheral to the cell nucleus region. Ingeneral, in the cell image, the cell nucleus region has a high luminanceand a more distinct outline than the cytoplasm region peripheralthereto, and hence cell nucleus regions are easy to discriminate fromeach other, thus making it possible to easily enhance the divisionaccuracy. On the other hand, because the cytoplasm region adjoinsanother cytoplasm region, the boundary between cytoplasm regions isdifficult to identify, making it difficult to enhance the divisionaccuracy for the cytoplasm region, compared with the cell nucleusregion.

For this reason, the first region formation unit forms a first cellregion that has a luminance higher than the prescribed luminancethreshold value and that is proximate to the peak position, each of thefirst cell regions is used as an initial region in the region growingmethod, and furthermore, the second region formation unit forms a secondcell region including a region having a luminance equal to or less thanthe prescribed luminance threshold value without restricting to theproximity of the peak position such that the second cell region is usedas a limit of the region growing range, thereby making it possible toenhance the accuracy of the morphology of a cell on the basis of thesecond cell region while enhancing the cell region division accuracy onthe basis of the first cell region.

A second aspect of the present invention is a cell recognition deviceincluding: an image acquisition unit for acquiring a cell image formedby capturing an image of a cell cluster composed of a plurality ofcells; a feature value calculation unit for calculating a feature value,the feature value representing how likely a pixel value in each ofpixels in the cell image acquired by the image acquisition unit is to bean extreme value; a peak position detection unit for detecting, as peakpositions, pixel positions the feature value of which are greater than aprescribed feature value threshold value in the cell image; a peakposition recording unit for recording the peak positions detected by thepeak position detection unit; a cell region formation unit for forming,one at a time for the peak positions recorded by the peak positionrecording unit, a cell region on the basis of the distribution of thepixel values of a plurality of pixels included in a region peripheral tothe peak position in the cell image; a cell region positionidentification unit for identifying a center position of the cell regionformed by the cell region formation unit; and a proximity statedetermination unit for determining, by using at least one of the peakposition, a morphology of the cell region, and the center position ofthe cell region, a proximity state between the cell region currentlyformed by the cell region formation unit and a previously formed cellregion, wherein, when the proximity state determination unit determinesthat the proximity state is satisfied, the cell region formation unitcorrects at least one of the currently formed cell region and thepreviously formed cell region.

According to this aspect, individual cells can be extracted with highaccuracy from the cell image formed by capturing, with the imageacquisition unit, an image of the cell cluster composed of the pluralityof living cells.

In the above-described aspect, the cell region formation unit mayinclude a first correction unit that does not perform the formation ofthe cell region when the proximity state determination unit determinesthat the distance between the peak position of the currently formed cellregion and the peak position of the previously formed cell region isequal to or less than a prescribed peak threshold value, and the peakposition recording unit may delete, from the record, the peak positionof the currently formed cell region for which it has been determined bythe proximity state determination unit that the distance between thepeak position of the currently formed cell region and the peak positionof the previously formed cell region is equal to or less than theprescribed peak threshold value.

In the above-described aspect, when the proximity state determinationunit determines that the distance between the peak position of thecurrently formed cell region and the peak position of the previouslyformed cell region is equal to or less than the prescribed peakthreshold value, the first correction unit may correct the centerposition of the previously formed cell region on the basis of the peakposition of the currently formed cell region.

In the above-described aspect, the cell region formation unit mayinclude a second correction unit that, when the proximity statedetermination unit determines that the distance between the peakposition of the currently formed cell region and the peak position ofthe previously formed cell region is larger than a prescribed peakthreshold value and that the distance between the center position of thecurrently formed cell region and the center position of the previouslyformed cell region is equal to or less than a prescribed inter-centerposition threshold value that is larger than the prescribed peakthreshold value, forms a new cell region by integrating the two cellregions, and the cell region position identification unit may identify acenter position of the cell region newly formed by the second correctionunit.

In the above-described aspect, the inter-center position threshold valuemay be set on the basis of the feature value of the corresponding peakposition in the cell region.

In the above-described aspect, the cell region formation unit mayinclude a third correction unit that, when the proximity statedetermination unit determines that the distance between the peakposition of the currently formed cell region and the peak position ofthe previously formed cell region is greater than a prescribed peakthreshold value and that the distance between the center position of thecurrently formed cell region and the center position of the previouslyformed cell region is greater than a prescribed inter-center positionthreshold value, which is greater than the prescribed peak thresholdvalue, but determines that the center position of the currently formedcell region overlaps the previously formed cell region or that thecurrently formed cell region overlaps the center position of thepreviously formed cell region, forms or re-forms the two cell regions soas not to overlap each other, and the cell region positionidentification unit may identify center positions of the two cellregions formed or re-formed by the third correction unit.

In the above-described aspect, the third correction unit may form orre-form the two cell regions by using a region growing method.

In the above-described aspect, the feature value calculation unit maycalculate the feature value from the pixel values included in a regionin a prescribed range set on the basis of a point spread function of anoptical system used to acquire the cell image.

In the above-described aspect, the feature value calculation unit maycalculate the feature value from the pixel values included in a regionin a prescribed range set on the basis of an apparent extension rate inZ direction of morphologies of the cells in the cell image.

In the above-described aspect, the cell region formation unit may set arange of the region peripheral to the peak position on the basis of anapparent extension rate in Z direction of morphologies of the cells inthe cell image.

The above-described aspect may further include a setting unit forallowing a user to set the extension rate in Z direction.

A third aspect of the present invention is a cell recognition methodincluding: a feature value calculation step of calculating a featurevalue, the feature value representing how likely a pixel value in eachof pixels in a cell image formed by capturing an image of a cell clustercomposed of a plurality of cells is to be an extreme value; a peakposition recording step of detecting, as peak positions, pixel positionsthe feature value of which are greater than a prescribed feature valuethreshold value in the cell image and recording the peak positions; acell region formation step of forming, one at a time for the peakpositions recorded in the peak position recording step, a cell region onthe basis of a distribution of the pixel values of a plurality of pixelsincluded in a region peripheral to the peak position in the cell image;a cell region position identification step of identifying a centerposition of the cell region formed in the cell region formation step;and a proximity state determination step of determining, by using atleast one of the peak position, a morphology of the cell region, and thecenter position of the cell region, a proximity state between the cellregion currently formed in the cell region formation step and apreviously formed cell region, wherein, when it is determined that theproximity state is satisfied in the proximity state determination step,at least one of the currently formed cell region and the previouslyformed cell region is corrected in the cell region formation step.

According to this aspect, the region of each of the plurality of cellsconstituting the cell cluster can be extracted by forming, in the cellregion formation step, a cell region on the basis of the distribution ofthe pixel values in a region peripheral to each of the peak positions,which are pixel positions having a feature value (representing howlikely the pixel value is to be an extreme value) that is calculated inthe feature value calculation step that is larger than the prescribedfeature value threshold value.

In this case, even if neighboring cell regions partially overlap eachother, the individual cell regions can be delimited by identifying thecenter positions of the cell regions in the cell region positionidentification step and correcting at least one of the currently formedcell region and the previously formed cell region in the cell regionformation step when it is determined in the proximity statedetermination step by using at least one of the peak position, themorphology of the cell region, and the center position of the cellregion that the currently formed cell region is proximate to thepreviously formed cell region. Therefore, individual cells can beextracted with high accuracy from the cell image formed by capturing animage of the cell cluster composed of a plurality of living cells.

In the above-described aspect, the cell region formation step mayinclude: a first region formation step of forming a first cell region onthe basis of a luminance distribution such that the first cell region isrestricted to a region that is proximate to the peak position and thathas a luminance higher than a prescribed luminance threshold value; asecond region formation step of forming, on the basis of the luminancedistribution, a second cell region including a region having a luminanceequal to or less than the prescribed luminance threshold value such thatthe second cell region is not restricted to the proximity of the peakposition; and a region growing step of setting, as an initial region,each of the first cell regions formed for all of the peak positions inthe first region formation step and causing the initial region to growsuch that a region growing range is restricted to the second cell regionformed in the second region formation step.

With this structure, a first cell region that has a luminance higherthan the prescribed luminance threshold value and that is proximate tothe peak position is formed in the first region formation step, each ofthe first cell regions is used as an initial region in the regiongrowing method, and furthermore, a second cell region including a regionhaving a luminance equal to or less than the prescribed luminancethreshold value is formed in the second region formation step withoutrestricting to the proximity of the peak such that the second cellregion is used as a limit of the region growing range. By doing so, itis possible to enhance the accuracy of the morphology of a cell on thebasis of the second cell region while enhancing the cell region divisionaccuracy on the basis of the first cell region.

A fourth aspect of the present invention is a cell recognition programfor causing a computer to execute: a feature value calculation step ofcalculating a feature value, the feature value representing how likely apixel value in each of pixels in a cell image formed by capturing animage of a cell cluster composed of a plurality of cells is to be anextreme value; a peak position recording step of detecting, as peakpositions, pixel positions the feature value of which are greater than aprescribed feature value threshold value in the cell image and recordingthe peak positions; a cell region formation step of forming, one at atime for the peak positions recorded in the peak position recordingstep, a cell region on the basis of a distribution of the pixel valuesof a plurality of pixels included in a region peripheral to the peakposition in the cell image; a cell region position identification stepof identifying a center position of the cell region formed in the cellregion formation step; and a proximity state determination step ofdetermining, by using at least one of the peak position, a morphology ofthe cell region, and the center position of the cell region, a proximitystate between the cell region currently formed in the cell regionformation step and a previously formed cell region, wherein, when it isdetermined that the proximity state is satisfied in the proximity statedetermination step, at least one of the currently formed cell region andthe previously formed cell region is corrected in the cell regionformation step.

According to this aspect, when the computer is executed, the region ofeach of the plurality of cells constituting the cell cluster can beextracted by forming, in the cell region formation step, a cell regionon the basis of the distribution of the pixel values in a regionperipheral to each of the peak positions having a high feature valuecalculated in the feature value calculation step.

In this case, when it is determined in the proximity state determinationstep by using at least one of the center position of the cell regionidentified in the cell region position identification step, the peakposition, and the morphology of the cell region that the currentlyformed cell region is proximate to the previously formed cell region,the individual cell regions can be delimited by correcting at least oneof the currently formed cell region and the previously formed cellregion in the cell region formation step. Therefore, when the computeris executed, individual cells can be extracted with high accuracy fromthe cell image formed by capturing an image of the cell cluster composedof the plurality of living cells.

In the above-described aspect, the cell region formation step mayinclude: a first region formation step of forming a first cell region onthe basis of a luminance distribution such that the first cell region isrestricted to a region that is proximate to the peak position and thathas a luminance higher than a prescribed luminance threshold value; asecond region formation step of forming, on the basis of the luminancedistribution, a second cell region including a region having a luminanceequal to or less than the prescribed luminance threshold value such thatthe second cell region is not restricted to the proximity of the peakposition; and a region growing step of setting, as an initial region,each of the first cell regions formed for all of the peak positions inthe first region formation step and causing the initial region to growsuch that a region growing range is restricted to the second cell regionformed in the second region formation step.

(Additional Item 1)

An image processing device comprising:

a memory; and

a processor comprising hardware, the processor configured to:

-   -   calculate a feature value, the feature value representing how        likely a pixel value in each of pixels in a cell image formed by        capturing an image of a cell cluster composed of a plurality of        cells is to be an extreme value;    -   detect, as peak positions, pixel positions the feature value of        which are greater than a prescribed feature value threshold        value in the cell image;    -   record the detected peak positions in the memory;    -   form, one at a time for the recorded peak positions recorded in        the memory, a cell region on the basis of a distribution of the        pixel values of a plurality of pixels included in a region        peripheral to the peak position in the cell image;    -   identify a center position of the formed cell region;    -   determine, by using at least one of the peak position, a        morphology of the cell region, and the center position of the        cell region, a proximity state between the currently formed cell        region and a previously formed cell region; and    -   correct, when it is determined that the proximity state is        satisfied, at least one of the currently formed cell region and        the previously formed cell region.        (Additional Item 2)

The image processing device according to Additional Item 1, wherein theprocessor is configured to:

not perform the formation of the cell region when it is determined thatthe distance between the peak position of the currently formed cellregion and the peak position of the previously formed cell region isequal to or less than a prescribed peak threshold value; and

delete, from the record, the peak position of the currently formed cellregion for which it has been determined that the distance between thepeak position of the currently formed cell region and the peak positionof the previously formed cell region is equal to or less than theprescribed peak threshold value.

(Additional Item 3)

The image processing device according to Additional Item 2, wherein theprocessor is configured to correct, when it is determined that thedistance between the peak position of the currently formed cell regionand the peak position of the previously formed cell region is equal toor less than the prescribed peak threshold value, the center position ofthe previously formed cell region on the basis of the peak position ofthe currently formed cell region.

(Additional Item 4)

The image processing device according one of Additional Items 1 to 3,wherein the processor is configured to:

form, when it is determined that the distance between the peak positionof the currently formed cell region and the peak position of thepreviously formed cell region is larger than a prescribed peak thresholdvalue and that the distance between the center position of the currentlyformed cell region and the center position of the previously formed cellregion is equal to or less than a prescribed inter-center positionthreshold value that is larger than the prescribed peak threshold value,a new cell region by integrating the two cell regions; and

identify a center position of the newly formed cell region.

(Additional Item 5)

The image processing device according to Additional Item 4, wherein theinter-center position threshold value is set on the basis of the featurevalue of the corresponding peak position in the cell region.

(Additional Item 6)

The image processing device according to one of Additional Items 1 to 5,wherein the processor is configured to:

form or re-form, when it is determined that the distance between thepeak position of the currently formed cell region and the peak positionof the previously formed cell region is greater than a prescribed peakthreshold value and that the distance between the center position of thecurrently formed cell region and the center position of the previouslyformed cell region is greater than a prescribed inter-center positionthreshold value, which is greater than the prescribed peak thresholdvalue, but it is determined that the center position of the currentlyformed cell region overlaps the previously formed cell region or thatthe currently formed cell region overlaps the center position of thepreviously formed cell region, the two cell regions so as not to overlapeach other; and

identify center positions of the formed or re-formed two cell regions.

(Additional Item 7)

The image processing device according to Additional Item 6, wherein theprocessor is configured to form or re-form the two cell regions by usinga region growing method.

(Additional Item 8)

The image processing device according to one of Additional Items 1 to 7,wherein the processor is configured to delete the peak position thepixel value of which is equal to or less than a prescribed pixelthreshold value.

(Additional Item 9)

The image processing device according to Additional Item 8, wherein theprescribed pixel threshold value is set on the basis of a backgroundvalue estimated from the distribution of the pixel values of the cellimage.

(Additional Item 10)

The image processing device according to Additional Item 9, wherein thebackground value is estimated from a bimodal Gaussian distributionfitted to the distribution of the pixel values.

(Additional Item 11)

The image processing device according to one of Additional Items 1 to10, wherein the processor is configured to sort the recorded peakpositions by the feature value and delete, when the distance between twoneighboring peak positions of the peak positions is equal to or lessthan a prescribed proximity threshold value, either one of the twoneighboring peak positions from the record.

(Additional Item 12)

The image processing device according to one of Additional Items 1 to11, wherein the processor is configured to calculate, as the featurevalue, a LoG (Laplacian of Gaussian) filter output value in response tothe cell image.

(Additional Item 13)

The image processing device according to one of Additional Items 1 to12, wherein the processor is configured to form the cell region bybinarizing the plurality of pixels included in the region peripheral tothe peak position with reference to a pixel value distribution thresholdvalue that is set on the basis of the distribution of the pixel valuesof the plurality of pixels.

(Additional Item 14)

The image processing device according to Additional Item 13, wherein theprocessor is configured to smooth the plurality of pixels included inthe region peripheral to the peak position and binarizes the pluralityof smoothed pixels.

(Additional Item 15)

The image processing device according to one of Additional Items 13 and14, wherein the processor is configured to set a prescribed adaptivebinarization threshold value on the basis of the distribution of thepixel values and binarize the plurality of pixels on the basis of thebinarization threshold value.

(Additional Item 16)

The image processing device according to Additional Item 15, wherein theprescribed binarization threshold value is reset when the prescribedbinarization threshold value is smaller, by a prescribed amount or more,than a maximum value of the pixel values of the plurality of pixelsincluded in the region peripheral to the peak position.

(Additional Item 17)

The image processing device according to one of Additional Items 1 to16, wherein the processor is configured to set a range of the regionperipheral to the peak position on the basis of the feature value of thepeak position.

(Additional Item 18)

The image processing device according to one of Additional Items 1 to17, wherein the processor is configured to fill a hole generated in theformed cell region.

(Additional Item 19)

The image processing device according to one of Additional Items 1 to18, wherein the processor is configured to smooth a boundary of the cellregion.

(Additional Item 20)

The image processing device according to one of Additional Items 1 to19, wherein when the cell region is a region divided into a plurality ofcell regions, the processor is configured to keep the cell regionclosest to the corresponding peak position and delete the other cellregions.

(Additional Item 21)

The image processing device according to one of Additional Items 1 to20, wherein formation of the cell region and identification of thecenter position are performed concurrently on peak positions differentfrom each other.

(Additional Item 22)

The image processing device according to one of Additional Items 1 to21, wherein the processor is configured to calculate the feature valuefrom the pixel values included in a region in a prescribed range set onthe basis of a point spread function of an optical system used toacquire the cell image.

(Additional Item 23)

The image processing device according to one of Additional Items 1 to21, wherein the processor is configured to calculate the feature valuefrom the pixel values included in a region in a prescribed range set onthe basis of an apparent extension rate in Z direction of morphologiesof the cells in the cell image.

(Additional Item 24)

The image processing device according to one of Additional Items 1 to23, wherein the processor is configured to set a range of the regionperipheral to the peak position on the basis of an apparent extensionrate in Z direction of morphologies of the cells in the cell image.

(Additional Item 25)

The image processing device according to Additional Item 24, wherein theprocessor is configured to allow a user to set the extension rate in Zdirection.

(Additional Item 26)

The image processing device according to one of Additional Items 1 to25, wherein the processor is configured to:

form a first cell region on the basis of a luminance distribution suchthat the first cell region is restricted to a region that is proximateto the peak position and that has a luminance higher than a prescribedluminance threshold value;

form, on the basis of the luminance distribution, a second cell regionincluding a region having a luminance equal to or less than theprescribed luminance threshold value such that the second cell region isnot restricted to the proximity of the peak position; and

set, as an initial region, each of the first cell regions formed for allof the peak positions and cause the initial region to grow such that aregion growing range is restricted to the formed second cell region.

(Additional Item 27)

A cell recognition device comprising:

an image acquisition device that is configured to acquire a cell imageformed by capturing an image of a cell cluster composed of a pluralityof cells; and

an image processing device that includes a memory and a processor, theprocessor configured to:

-   -   calculate a feature value, the feature value representing how        likely a pixel value in each of pixels in the cell image        acquired by the image acquisition device is to be an extreme        value;    -   detect, as peak positions, pixel positions the feature value of        which are greater than a prescribed feature value threshold        value in the cell image;    -   record the detected peak positions in the memory;    -   form, one at a time for the recorded peak positions recorded in        the memory, a cell region on the basis of a distribution of the        pixel values of a plurality of pixels included in a region        peripheral to the peak position in the cell image;    -   identify a center position of the formed cell region;    -   determine, by using at least one of the peak position, a        morphology of the cell region, and the center position of the        cell region, a proximity state between the currently formed cell        region and a previously formed cell region; and    -   correct, when it is determined that the proximity state is        satisfied, at least one of the currently formed cell region and        the previously formed cell region.        (Additional Item 28)

The cell recognition device according to Additional Item 27, wherein theprocessor is configured to:

not perform the formation of the cell region when it is determined thatthe distance between the peak position of the currently formed cellregion and the peak position of the previously formed cell region isequal to or less than a prescribed peak threshold value; and

delete, from the record, the peak position of the currently formed cellregion for which it has been determined that the distance between thepeak position of the currently formed cell region and the peak positionof the previously formed cell region is equal to or less than theprescribed peak threshold value.

(Additional Item 29)

The cell recognition device according to Additional Item 28, wherein theprocessor is configured to correct, when it is determined that thedistance between the peak position of the currently formed cell regionand the peak position of the previously formed cell region is equal toor less than the prescribed peak threshold value, the center position ofthe previously formed cell region on the basis of the peak position ofthe currently formed cell region.

(Additional Item 30)

The cell recognition device according to one of Additional Items 27 to29, wherein the processor is configured to:

form, when it is determined that the distance between the peak positionof the currently formed cell region and the peak position of thepreviously formed cell region is larger than a prescribed peak thresholdvalue and that the distance between the center position of the currentlyformed cell region and the center position of the previously formed cellregion is equal to or less than a prescribed inter-center positionthreshold value that is larger than the prescribed peak threshold value,a new cell region by integrating the two cell regions; and

identify a center position of the newly formed cell region.

(Additional Item 31)

The cell recognition device according to Additional Item 30, wherein theinter-center position threshold value is set on the basis of the featurevalue of the corresponding peak position in the cell region.

(Additional Item 32)

The cell recognition device according to one of Additional Items 27 to31, wherein the processor is configured to:

form or re-form, when it is determined that the distance between thepeak position of the currently formed cell region and the peak positionof the previously formed cell region is greater than a prescribed peakthreshold value and that the distance between the center position of thecurrently formed cell region and the center position of the previouslyformed cell region is greater than a prescribed inter-center positionthreshold value, which is greater than the prescribed peak thresholdvalue, but it is determined that the center position of the currentlyformed cell region overlaps the previously formed cell region or thatthe currently formed cell region overlaps the center position of thepreviously formed cell region, the two cell regions so as not to overlapeach other; and

identify center positions of the formed or re-formed two cell regions.

(Additional Item 33)

The cell recognition device according to Additional Item 32, wherein theprocessor is configured to form or re-form the two cell regions by usinga region growing method.

(Additional Item 34)

The cell recognition device according to one of Additional Items 27 to33, wherein the processor is configured to calculate the feature valuefrom the pixel values included in a region in a prescribed range set onthe basis of a point spread function of an optical system used toacquire the cell image.

(Additional Item 35)

The cell recognition device according to one of Additional Items 27 to33, wherein the processor is configured to calculate the feature valuefrom the pixel values included in a region in a prescribed range set onthe basis of an apparent extension rate in Z direction of morphologiesof the cells in the cell image.

(Additional Item 36)

The cell recognition device according to one of Additional Items 27 to35, wherein the processor is configured to set a range of the regionperipheral to the peak position on the basis of an apparent extensionrate in Z direction of morphologies of the cells in the cell image.

(Additional Item 37)

The cell recognition device according to Additional Item 36, wherein theprocessor is configured to allow a user to set the extension rate in Zdirection.

(Additional Item 38)

A cell recognition method comprising:

calculating a feature value, the feature value representing how likely apixel value in each of pixels in a cell image formed by capturing animage of a cell cluster composed of a plurality of cells is to be anextreme value;

detecting, as peak positions, pixel positions the feature value of whichare greater than a prescribed feature value threshold value in the cellimage and recording the detected peak positions;

forming, one at a time for the recorded peak positions, a cell region onthe basis of a distribution of the pixel values of a plurality of pixelsincluded in a region peripheral to the peak position in the cell image;

identifying a center position of the formed cell region;

determining, by using at least one of the peak position, a morphology ofthe cell region, and the center position of the cell region, a proximitystate between the currently formed cell region and a previously formedcell region; and

correcting, when it is determined that the proximity state is satisfied,at least one of the currently formed cell region and the previouslyformed cell region.

(Additional Item 39)

The cell recognition method according to Additional Item 38, furtherincluding:

forming a first cell region on the basis of a luminance distributionsuch that the first cell region is restricted to a region that isproximate to the peak position and that has a luminance higher than aprescribed luminance threshold value;

forming, on the basis of the luminance distribution, a second cellregion including a region having a luminance equal to or less than theprescribed luminance threshold value such that the second cell region isnot restricted to the proximity of the peak position; and

setting, as an initial region, each of the first cell regions formed forall of the peak positions and causing the initial region to grow suchthat a region growing range is restricted to the formed second cellregion.

(Additional Item 40)

A non-transitory computer-readable medium having a cell recognitionprogram stored thereon, the cell recognition program causing a computerto execute functions of:

calculating a feature value, the feature value representing how likely apixel value in each of pixels in a cell image formed by capturing animage of a cell cluster composed of a plurality of cells is to be anextreme value;

detecting, as peak positions, pixel positions the feature value of whichare greater than a prescribed feature value threshold value in the cellimage and recording the peak positions;

forming, one at a time for the recorded peak positions, a cell region onthe basis of a distribution of the pixel values of a plurality of pixelsincluded in a region peripheral to the peak position in the cell image;

identifying a center position of the formed cell region;

determining, by using at least one of the peak position, a morphology ofthe cell region, and the center position of the cell region, a proximitystate between the currently formed cell region and a previously formedcell region; and

correcting, when it is determined that the proximity state is satisfied,at least one of the currently formed cell region and the previouslyformed cell region.

(Additional Item 41)

The non-transitory computer-readable medium according to Additional Item40, wherein the cell recognition program further including functions of:

forming a first cell region on the basis of a luminance distributionsuch that the first cell region is restricted to a region that isproximate to the peak position and that has a luminance higher than aprescribed luminance threshold value;

forming, on the basis of the luminance distribution, a second cellregion including a region having a luminance equal to or less than theprescribed luminance threshold value such that the second cell region isnot restricted to the proximity of the peak position; and

setting, as an initial region, each of the first cell regions formed forall of the peak positions and causing the initial region to grow suchthat a region growing range is restricted to the formed second cellregion.

REFERENCE SIGNS LIST

-   1, 61 Cell recognition device-   3 Image acquisition device (image acquisition unit)-   5 Image processing device-   9 Expansion parameter setting unit (setting unit)-   13, 63 Region generation unit (cell region formation unit)-   19 LoG filter unit (feature value calculation unit)-   21 Seed detection processing unit (peak position detection unit)-   27 Seed recording unit (peak position recording unit)-   35 First overlapping determination unit (proximity state    determination unit)-   37 Cell position correction unit (first correction unit)-   39 First region formation unit (cell region formation unit, hole    filling processing unit, fragment-region removing unit)-   41 First region position setting unit (cell region position    identification unit)-   45 Threshold value processing unit (proximity state determination    unit)-   47 Region integration processing unit (cell region formation unit,    second correction unit)-   49 Second region position setting unit (cell region position    identification unit)-   51 Second overlapping determination unit (proximity state    determination unit)-   53 Second region formation unit (cell region formation unit, third    correction unit)-   55 Third region position setting unit (cell region position    identification unit)-   65 Region growing unit-   67 First region formation unit (cell region formation unit, first    region formation unit, second region formation unit)-   69 Cytoplasm region recording unit-   SA3 LoG filter processing step (feature value calculation step)-   SA5 Seed detection processing step (peak position recording step)-   SA8, SA8′ Cell region formation step-   SB1, SB7, SB10 Proximity state determination step-   SB5, SB9, SB12 Cell region position identification step-   C Cell

The invention claimed is:
 1. An image processing device comprising: amemory; and a processor comprising hardware, the processor configuredto: calculate a feature value, the feature value representing alikelihood of a pixel value in each of pixels in a cell image formed bycapturing an image of a cell cluster composed of a plurality of cellsbeing an extreme value; detect, as peak positions, pixel positions thefeature value of which are greater than a prescribed feature valuethreshold value in the cell image; record the detected peak positions inthe memory; form, one at a time for the recorded peak positions recordedin the memory, a cell region on the basis of a distribution of the pixelvalues of a plurality of pixels included in a region peripheral to thepeak position in the cell image; identify a center position of theformed cell region; determine, by using at least one of the peakposition, a morphology of the cell region, and the center position ofthe cell region, a proximity state between the currently formed cellregion and a previously formed cell region; and correct, when it isdetermined that the proximity state is satisfied, at least one of thecurrently formed cell region and the previously formed cell region,wherein formation of the cell region and identification of the centerposition are performed concurrently on peak positions different fromeach other.
 2. The image processing device according to claim 1, whereinthe processor is configured to: not perform the formation of the cellregion when it is determined that the distance between the peak positionof the currently formed cell region and the peak position of thepreviously formed cell region is equal to or less than a prescribed peakthreshold value; and delete, from the record, the peak position of thecurrently formed cell region for which it has been determined that thedistance between the peak position of the currently formed cell regionand the peak position of the previously formed cell region is equal toor less than the prescribed peak threshold value.
 3. The imageprocessing device according to claim 1, wherein the processor isconfigured to: form, when it is determined that the distance between thepeak position of the currently formed cell region and the peak positionof the previously formed cell region is larger than a prescribed peakthreshold value and that the distance between the center position of thecurrently formed cell region and the center position of the previouslyformed cell region is equal to or less than a prescribed inter-centerposition threshold value that is larger than the prescribed peakthreshold value, a new cell region by integrating the two cell regions;and identify a center position of the newly formed cell region.
 4. Theimage processing device according to claim 1, wherein the processor isconfigured to: form or re-form, when it is determined that the distancebetween the peak position of the currently formed cell region and thepeak position of the previously formed cell region is greater than aprescribed peak threshold value and that the distance between the centerposition of the currently formed cell region and the center position ofthe previously formed cell region is greater than a prescribedinter-center position threshold value, which is greater than theprescribed peak threshold value, but it is determined that the centerposition of the currently formed cell region overlaps the previouslyformed cell region or that the currently formed cell region overlaps thecenter position of the previously formed cell region, the two cellregions so as not to overlap each other; and identify center positionsof the formed or re-formed two cell regions.
 5. The image processingdevice according to claim 1, wherein the processor is configured to sortthe recorded peak positions by the feature value and delete, when thedistance between two neighboring peak positions of the peak positions isequal to or less than a prescribed proximity threshold value, either oneof the two neighboring peak positions from the record.
 6. The imageprocessing device according to claim 1, wherein the processor isconfigured to form the cell region by binarizing the plurality of pixelsincluded in the region peripheral to the peak position with reference toa pixel value distribution threshold value that is set on the basis ofthe distribution of the pixel values of the plurality of pixels.
 7. Theimage processing device according to claim 1, wherein the processor isconfigured to calculate the feature value from the pixel values includedin a region in a prescribed range set on the basis of a point spreadfunction of an optical system used to acquire the cell image.
 8. Theimage processing device according to claim 1, wherein the processor isconfigured to calculate the feature value from the pixel values includedin a region in a prescribed range set on the basis of an apparentextension rate in Z direction of morphologies of the cells in the cellimage.
 9. The image processing device according to claim 8, wherein theprescribed pixel threshold value is set on the basis of a backgroundvalue estimated from the distribution of the pixel values of the cellimage.
 10. The image processing device according to claim 1, wherein theprocessor is configured to: form a first cell region on the basis of aluminance distribution such that the first cell region is restricted toa region that is proximate to the peak position and that has a luminancehigher than a prescribed luminance threshold value; form, on the basisof the luminance distribution, a second cell region including a regionhaving a luminance equal to or less than the prescribed luminancethreshold value such that the second cell region is not restricted tothe proximity of the peak position; and set, as an initial region, eachof the first cell regions formed for all of the peak positions and causethe initial region to grow such that a region growing range isrestricted to the formed second cell region.
 11. The image processingdevice according to claim 10, wherein the background value is estimatedfrom a bimodal Gaussian distribution fitted to the distribution of thepixel values.
 12. The image processing device according to claim 1,wherein the processor is configured to calculate, as the feature value,a Laplacian of Gaussian (LoG) filter output value in response to thecell image.
 13. The image processing device according to claim 1,wherein the processor is configured to delete the peak position thepixel value of which is equal to or less than a prescribed pixelthreshold value.
 14. The image processing device according to claim 1,wherein the processor is configured to set a range of the regionperipheral to the peak position on the basis of the feature value of thepeak position.
 15. The image processing device according to claim 1,wherein the processor is configured to fill a hole generated in theformed cell region.
 16. A cell recognition method comprising:calculating a feature value, the feature value representing a likelihoodof a pixel value in each of pixels in a cell image formed by capturingan image of a cell cluster composed of a plurality of cells being anextreme value; detecting, as peak positions, pixel positions the featurevalue of which are greater than a prescribed feature value thresholdvalue in the cell image and recording the detected peak positions;forming, one at a time for the recorded peak positions, a cell region onthe basis of a distribution of the pixel values of a plurality of pixelsincluded in a region peripheral to the peak position in the cell image;identifying a center position of the formed cell region; determining, byusing at least one of the peak position, a morphology of the cellregion, and the center position of the cell region, a proximity statebetween the currently formed cell region and a previously formed cellregion; and correcting, when it is determined that the proximity stateis satisfied, at least one of the currently formed cell region and thepreviously formed cell region, wherein formation of the cell region andidentification of the center position are performed concurrently on peakpositions different from each other.
 17. The cell recognition methodaccording to claim 16, further including: forming a first cell region onthe basis of a luminance distribution such that the first cell region isrestricted to a region that is proximate to the peak position and thathas a luminance higher than a prescribed luminance threshold value;forming, on the basis of the luminance distribution, a second cellregion including a region having a luminance equal to or less than theprescribed luminance threshold value such that the second cell region isnot restricted to the proximity of the peak position; and setting, as aninitial region, each of the first cell regions formed for all of thepeak positions and causing the initial region to grow such that a regiongrowing range is restricted to the formed second cell region.
 18. Anon-transitory computer-readable medium having a cell recognitionprogram stored thereon, the cell recognition program causing a computerto execute functions of: calculating a feature value, the feature valuerepresenting a likelihood of a pixel value in each of pixels in a cellimage formed by capturing an image of a cell cluster composed of aplurality of cells being an extreme value; detecting, as peak positions,pixel positions the feature value of which are greater than a prescribedfeature value threshold value in the cell image and recording the peakpositions; forming, one at a time for the recorded peak positions, acell region on the basis of a distribution of the pixel values of aplurality of pixels included in a region peripheral to the peak positionin the cell image; identifying a center position of the formed cellregion; determining, by using at least one of the peak position, amorphology of the cell region, and the center position of the cellregion, a proximity state between the currently formed cell region and apreviously formed cell region; and correcting, when it is determinedthat the proximity state is satisfied, at least one of the currentlyformed cell region and the previously formed cell region, whereinformation of the cell region and identification of the center positionare performed concurrently on peak positions different from each other.19. The non-transitory computer-readable medium according to claim 18,wherein the cell recognition program further including functions of:forming a first cell region on the basis of a luminance distributionsuch that the first cell region is restricted to a region that isproximate to the peak position and that has a luminance higher than aprescribed luminance threshold value; forming, on the basis of theluminance distribution, a second cell region including a region having aluminance equal to or less than the prescribed luminance threshold valuesuch that the second cell region is not restricted to the proximity ofthe peak position; and setting, as an initial region, each of the firstcell regions formed for all of the peak positions and causing theinitial region to grow such that a region growing range is restricted tothe formed second cell region.